Radar Toolbox Release Notes

How to Contact MathWorks

Latest news: www.mathworks.com

Sales and services: www.mathworks.com/sales_and_services

User community: www.mathworks.com/matlabcentral

Technical support: www.mathworks.com/support/contact_us

Phone: 508-647-7000

The MathWorks, Inc.

1 Apple Hill Drive
Natick, MA 01760-2098
Radar Toolbox Release Notes
© COPYRIGHT 2021–2025 by The MathWorks, Inc.
The software described in this document is furnished under a license agreement. The software may be used or copied
only under the terms of the license agreement. No part of this manual may be photocopied or reproduced in any form
without prior written consent from The MathWorks, Inc.
FEDERAL ACQUISITION: This provision applies to all acquisitions of the Program and Documentation by, for, or through
the federal government of the United States. By accepting delivery of the Program or Documentation, the government
hereby agrees that this software or documentation qualifies as commercial computer software or commercial computer
software documentation as such terms are used or defined in FAR 12.212, DFARS Part 227.72, and DFARS 252.227-7014.
Accordingly, the terms and conditions of this Agreement and only those rights specified in this Agreement, shall pertain
to and govern the use, modification, reproduction, release, performance, display, and disclosure of the Program and
Documentation by the federal government (or other entity acquiring for or through the federal government) and shall
supersede any conflicting contractual terms or conditions. If this License fails to meet the government's needs or is
inconsistent in any respect with federal procurement law, the government agrees to return the Program and
Documentation, unused, to The MathWorks, Inc.
Trademarks
MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See
www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be
trademarks or registered trademarks of their respective holders.
Patents
MathWorks products are protected by one or more U.S. patents. Please see www.mathworks.com/patents for
more information.
 Contents

R2025a

bistaticTransmitter and bistaticReceiver Objects: Simulate bistatic radars

.......................................................... 1-2

freespacePath and bistaticFreeSpacePath Functions: Calculate free space

propagation paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

Radar Designer App: New functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

Surface clutter simulation and modeling capabilities . . . . . . . . . . . . . . . . 1-4

Applications: Bistatic radar, AI, data synthesis, signal and data

processing, and HDL code generation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

Functionality being removed or changed . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5

Radar Equation Calculator will be removed . . . . . . . . . . . . . . . . . . . . . . . 1-5

R2024b

Radar Designer App: Generate MATLAB scripts that export measurement-

level radar data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2

bistaticConstantSNR Function: Create bistatic constant SNR contours or

surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2

rcscylinder Function: New Knott model . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

radarDataGenerator: Random number generator updated . . . . . . . . . . . . 2-3

Applications: AI, bistatic radar, data synthesis, and radar systems

engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

Radar Toolbox Support Package for Texas Instruments mmWave Radar

Sensors: Acquire raw ADC data from TI mmWave radar sensor using
DCA1000EVM capture card . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

Functionality being removed or changed . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

Radar Equation Calculator will be removed . . . . . . . . . . . . . . . . . . . . . . . 2-4

iii
 R2024a

Multipath support for radarScenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

Complex scattering matrix support for radarScenario . . . . . . . . . . . . . . . . 3-2

Plot custom surface reflectivity maps in radarScenario . . . . . . . . . . . . . . . 3-2

Atmospheric refraction effects updated for radarScenario . . . . . . . . . . . . 3-2

weatherTimeSeries Function: Simulate weather radar returns . . . . . . . . . 3-2

Radar Designer App: Radar selection table . . . . . . . . . . . . . . . . . . . . . . . . 3-2

earthSurfacePermittivityFunction: Model materials update . . . . . . . . . . . 3-2

Coordinate system conventions and reference frames used in Radar

Toolbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

Applications: AI, GPU acceleration, radar models, multipath, and tracking

.......................................................... 3-3

Radar Toolbox Support Package for Texas Instruments mmWave Radar

Sensors: Support for IWRL6432BOOST . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3

R2023b

Radar Designer app changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2

Polarimetric IQ data generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2

Clutter Sample Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2

Applications: Simulate radar returns from wind turbine . . . . . . . . . . . . . . 4-2

Radar Toolbox Support Package for Texas Instruments mmWave Radar

Sensors: Acquire data from Texas Instruments mmWave radar sensors
.......................................................... 4-2

Radar Toolbox Support Package for Texas Instruments mmWave Radar

Sensors: Support for IWRL6432BOOST (October 2023, Version 23.2.1)
.......................................................... 4-3

iv Contents
 R2023a

Improvements to surface scattering and clutter simulations . . . . . . . . . . 5-2

Create radar blind zone maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-2

Polarization support for surfaces and targets . . . . . . . . . . . . . . . . . . . . . . . 5-2

Radar range limits for radarTransceiver System object . . . . . . . . . . . . . . . 5-3

Extended target support for radarTransceiver . . . . . . . . . . . . . . . . . . . . . . 5-3

Radar scenario atmosphere models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3

Automatic unit conversion in Radar Designer app . . . . . . . . . . . . . . . . . . . 5-3

Applications: Surface clutter, Pulsed radar, MIMO radar . . . . . . . . . . . . . 5-3

R2022b

Range-Doppler radar cross section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-2

Scenario surface and clutter visualization . . . . . . . . . . . . . . . . . . . . . . . . . 6-2

Atmospheric refracted signal paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-2

New plot capabilities for Radar Designer app . . . . . . . . . . . . . . . . . . . . . . . 6-3

Radar budget plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3

Radar resource management: Workflow enhancements for

radarDataGenerator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3

Use motion model name to obtain track position, velocity, and covariance
.......................................................... 6-4

Confirm tracks in radarTracker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4

Specify wait and reverse motion for waypoint trajectory . . . . . . . . . . . . . . 6-4

Applications: Radar models, clutter, and atmospheric effects . . . . . . . . . 6-4

v
 R2022a

Land and sea surface modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2

Land and sea reflectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2

RCS fluctuation modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-3

Array scan loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-3

Wrap measurements in tracking filters to prevent filter divergence . . . . 7-3

Changes to behavior of trackingKF object . . . . . . . . . . . . . . . . . . . . . . . . . 7-4

Applications: Surface and clutter simulation, multifunction radar, SAR

image formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-4

R2021b

Radar Designer App: Plot vertical coverage diagrams . . . . . . . . . . . . . . . . 8-2

Synthetic Aperture Radar: Convert between ground range resolution and

slant range resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-2

Radar Data Generator block: Generate radar data in Simulink . . . . . . . . 8-2

New custom scan mode for radarDataGenerator . . . . . . . . . . . . . . . . . . . . 8-2

Merge detections into clustered detections using mergeDetections . . . . 8-2

Generate more memory-efficient C/C++ code from tracking filters . . . . . 8-3

Applications: AI, SAR, Tracking, Radar Coverage, Environment Effects

.......................................................... 8-3

R2021a

New Radar Toolbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-2

Radar Designer App: Model radar gains and losses and assess
performance in different environments . . . . . . . . . . . . . . . . . . . . . . . . . . 9-2

Evaluate Radar Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-2

vi Contents
Create Radar Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-2

Simulate Radar Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-2

Signal and Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-3

Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-3

vii
 1

R2025a

Version: 25.1

New Features

Compatibility Considerations
R2025a

bistaticTransmitter and bistaticReceiver Objects: Simulate bistatic

radars
Use bistaticTransmitter and bistaticReceiver to simulate bistatic radars. These objects
support synchronous and asynchronous transmitter and receiver pairs and custom propagation loss
functions.

1-2
1-3
R2025a

freespacePath and bistaticFreeSpacePath Functions: Calculate free

space propagation paths
Calculate free space propagation paths using freespacePath for monostatic radars and
bistaticFreeSpacePath for bistatic radars.

Radar Designer App: New functionality

• Import custom antenna patterns using the new Import Antenna button on the Radar Designer
toolstrip.
• Export Radar Data Generator MATLAB Script supports atmospheric refraction models
starting this release.
• Export Radar Data Generator MATLAB Script supports electronic and mechanical
scanning radar configurations starting this release.

Surface clutter simulation and modeling capabilities

“Radar Surface Clutter Simulation” provides an overview of clutter modeling capabilities in Radar
Toolbox organized by power-level, measurement-level, and waveform-level applications. The highest-
fidelity simulations model dynamic scenarios with moving targets and support site-specific terrain
models and atmospheric refraction.

Applications: Bistatic radar, AI, data synthesis, signal and data

processing, and HDL code generation
• The “Non-Cooperative Bistatic Radar I/Q Simulation and Processing” example simulates I/Q
samples for a non-cooperative passive bistatic radar system that has two different signals of
opportunity.
• The “Parallel Simulation of Target, Clutter, and Interference Signals” example simulates clutter
and interference using bistaticTransmitter and bistaticReceiver to model both halves of
a monostatic radar and speeds up the simulation using parallel processing.
• The “Augmenting Radar Data to Create More Diverse Datasets” example shows how to augment
real and synthetic radar data for AI applications.
• The “From ADC to AI: A Radar Data Deep Learning Tutorial” example demonstrates a full deep
learning workflow for a radar system, including how to process raw ADC data, create a labeled
dataset, and train a neural network on radar data.
• The “Create a Digital Twin of a TI mmWave Radar” example models a TI mmWave radar using
radarTransceiver, simulates time division multiplexing (TDM) and dechirping, and compares
real and synthetic data.
• The “Analysis and Simulation of a Low Probability of Intercept Radar System” example models a
low probability of intercept radar system and a non-cooperative receiver that aims to intercept
signals transmitted by the radar.
• The “Radar Target Emulator with HDL Coder” example demonstrates an HDL compatible radar
target emulator that can be deployed onto a field-programmable-gate-array (FPGA) to emulate
radar target returns in real time for hardware-in-the-loop (HIL) testing.
• Two examples have been updated to illustrate new capabilities:

1-4
 • The updated “Cooperative Bistatic Radar I/Q Simulation and Processing” example now uses
bistaticTransmitter and bistaticReceiver workflows.
• The updated “Radar Design Part I: From Power Budget Analysis to Dynamic Scenario
Simulation” now exports a scanning radar design to a measurement-level
radarDataGenerator MATLAB® script.

Functionality being removed or changed

Radar Equation Calculator will be removed
Warns

The Radar Equation Calculator app will be removed in a future release. Use the Radar Designer app
instead.

1-5
 2

R2024b

Version: 24.2

New Features

Compatibility Considerations
R2024b

Radar Designer App: Generate MATLAB scripts that export

measurement-level radar data
Generate MATLAB scripts that export measurement-level radar data from the Radar Designer app to
radarDataGenerator and radarScenario. Select Export Radar Data Generator MATLAB
Script under the Export tab to simulate a radar detecting a target in a dynamic, free-space
scenario.

bistaticConstantSNR Function: Create bistatic constant SNR

contours or surfaces
Visualize co-site, receiver-centered, and transmitter-centered bistatic operational regions using the
new bistaticConstantSNR function. Assess bistatic radar performance in a free-space
environment by plotting constant signal to noise ratio (SNR) contours or surfaces, also known as
Cassini ovals or surfaces.

2-2
rcscylinder Function: New Knott model
The Knott model, an additional radar cross section (RCS) physical optics model, is now available in
the rcscylinder function. Use the Knott model to simulate RCSs for circular cylinders that include
phase contributions from cylinder ends.

radarDataGenerator: Random number generator updated

The initial seed for random numbers generated by radarDataGenerator during random noise
calculations is now controlled by the rng function instead of RandStream.setGlobalStream and
therefore produces a different sequence of random numbers.

Applications: AI, bistatic radar, data synthesis, and radar systems

engineering
• The Improving Weather Radar Moment Estimation with Convolutional Neural Networks example
trains and evaluates convolutional neural networks (CNN) to improve weather radar moment
estimation.
• The Mission Gap Analysis for Upgrading a Radar System example conducts a mission engineering
trade study to select between two different modifications for an existing radar system.
• The Cooperative Bistatic Radar I/Q Simulation and Processing example simulates I/Q for a
cooperative bistatic system and performs subsequent signal processing for a single bistatic
transmitter and receiver pair.
• The Passive Bistatic Radar Performance Assessment example evaluates the performance of a
passive bistatic radar using a typical FM radio transmitter as the illuminator of opportunity.
• The Radar Design Part I: From Power Budget Analysis to Dynamic Scenario Simulation
example exports a power-level Radar Designer session to an equivalent measurement-level model
and simulates the radar and target in dynamic scenarios.
• The Radar Design Part II: From Radar Data to IQ Signal Synthesis example demonstrates
how to go from a measurement-level model of a radar to a waveform-level radar transceiver that
can model I/Q samples.
• The Dynamic Selection of Optimal High PRF Sets for Airborne Radar example performs waveform
design/optimization to determine a set of pulse repetition frequencies (PRFs) that maximize the
range of the first blind zone while maintaining a high probability of detection for a variety of
targets.

Radar Toolbox Support Package for Texas Instruments mmWave Radar

Sensors: Acquire raw ADC data from TI mmWave radar sensor using
DCA1000EVM capture card
Acquire and record raw ADC data (IQ signals) in real-time using a DCA1000EVM capture card
connected to one of the supported TI mmWave Radar Sensor EVMs. The Hardware Setup screens of
the support package are updated to set up and configure the TI mmWave radar sensor and the
DCA1000 card to interface with MATLAB.

Use the new dca1000 object to read raw ADC data for live processing. Record the IQ data for offline
processing by using the startRecording and stopRecording functions of the dca1000 object.
Then, use the new dca1000FileReader object and read function to read the IQ data as radar data
cubes from the recorded file.

2-3
R2024b

These examples cover the detailed workflow:

• Read and Process Raw ADC Data in Real-Time from TI mmWave Radar Board Using DCA1000EVM
Capture Card
• Record Raw ADC Data for Offline Processing from TI mmWave Radar Board Using DCA1000EVM
Capture Card
• I/Q Data Collection and Detection Generation with Texas Instruments (TI) millimeter-wave
(mmWave) Radar

Functionality being removed or changed

Radar Equation Calculator will be removed
Still runs

The Radar Equation Calculator app will be removed in a future release. Use the Radar Designer app
instead.

2-4
 3

R2024a

Version: 24.1

New Features

Bug Fixes

Compatibility Considerations
R2024a

Multipath support for radarScenario

Model interactions between target and ground surfaces using the new EnableMultipath property
of the SurfaceManager object function for all radarDataGenerator objects attached to platforms
in radarScenario. Enable multipath modeling to simulate signal fades and ghost targets over
landSurface and seaSurface objects in a free space atmosphere.

Complex scattering matrix support for radarScenario

Create custom surface objects for radar scenarios using the new customSurface object function of
radarScenario. Specify complex polarization scattering matrices with the Shh, Svv, Shv, and Svh
properties.

Plot custom surface reflectivity maps in radarScenario

Use the new plotReflectivityMap object function to plot surface reflectivity maps for custom
landSurface and seaSurface objects in radarScenario.

Atmospheric refraction effects updated for radarScenario

Atmospheric refraction effects modeled by the atmosphere object function of radarScenario have
been updated to apply to both surface clutter and target generation.

weatherTimeSeries Function: Simulate weather radar returns

Simulate weather returns using the new weatherTimeSeries function to generate fast and
customizable I/Q signals for monostatic polarimetric weather radar systems. Develop new weather
radar variable estimators, test signal processing algorithms, and evaluate radar system performance
using complex voltages returned in horizontal and vertical polarizations from weatherTimeSeries
simulations.

Radar Designer App: Radar selection table

Select and rename radar designs within the new Radars selection table of the Radar Designer app to
quickly assess and compare radar design viability.

earthSurfacePermittivityFunction: Model materials update

The earthSurfacePermittivity function now refers to pure ice as "pure-ice" instead of "dry-
ice". To update your code, replace instances of "dry-ice" with "pure-ice".

Coordinate system conventions and reference frames used in Radar

Toolbox
Radar Coordinate Systems and Frames defines conventions for constructing radar scenarios using
Radar Toolbox functionality. A sample radarScenario workflow demonstrates how to configure
platform and radarDataGenerator objects using global, body, and mounting coordinate frames.

3-2
Applications: AI, GPU acceleration, radar models, multipath, and
tracking
• The LPI Radar Waveform Classification Using Time-Frequency CNN example shows how to train a
time-frequency convolutional neural network (CNN) to classify received radar waveforms based
on their modulation scheme and evaluates the performance of the trained network across a range
of signal-to-noise (SNR) values.
• The Generate Novel Radar Waveforms Using GAN example generates new radar waveforms using
a Wasserstein generative adversarial network with a gradient penalty (WGAN-GP).
• The Simulating Radar Datacubes in Parallel shows how to simulate datacubes from an S-band
terminal airport surveillance radar serially and in parallel utilizing the Parallel Computing
Toolbox™.
• The Airborne Target Height Estimation Using Multipath Over Sea and Land example shows how to
simulate multipath from the Earth's surface in radarScenario and uses the multipath detections
generated by radarDataGenerator to estimate the height of airborne targets over both sea and
land.
• The Accelerating Radar Signal Processing Using GPU example compares the performance of a
conventional radar signal processing chain implemented in interpreted MATLAB and on a
graphical processing unit (GPU) using Parallel Computing Toolbox, MATLAB Coder™, and GPU
Coder™.
• The Grid-Based Tracking in Urban Environments Using Multiple Radars example shows how to
track moving objects with multiple high-resolution radars using a grid-based tracker to enable
fusion of data from high-resolution sensors such as radars and lidars.

Radar Toolbox Support Package for Texas Instruments mmWave Radar

Sensors: Support for IWRL6432BOOST
Radar Toolbox Support Package for Texas Instruments® mmWave Radar Sensors now provides
support for acquiring radar data from Texas Instruments IWRL6432BOOST mmWave radar sensor.
The Hardware Setup process detects the connected IWRL6432BOOST board and lets you complete
the initial configuration. You can then specify "TI IWRL6432BOOST" as the value for boardname
argument while creating the mmWaveRadar object and configure other properties to read detections
and other measurements from the IWRL6432BOOST board.

3-3
 4

R2023b

Version: 23.2

New Features

Bug Fixes
R2023b

Radar Designer app changes

The Radar Designer app is now built on JavaScript®. With this change, the app has an improved
interface and accelerated display of radar performance.

Polarimetric IQ data generation

Starting with this release you can use the radarTransceiver System object™ to generate
polarimetric radar IQ data. Use the new configureAntennas object function to set the antenna
polarizations as 'Combined'. This polarization mode lets you model the inherent polarization of a
sensor system.

Clutter Sample Indices

The new findClutterSampleIndices method of ClutterGenerator extracts subsets of
multidimensional signal data that correspond to surface clutter patches.

Applications: Simulate radar returns from wind turbine

• The example Simulating Radar Returns from a Wind Turbine Using Simple Scatterers
demonstrates how to simulate reflections from a wind turbine in a radar scenario. The example
models turbine blades by simple rectangular plates with known radar cross sections. IQ data is
generated and processed to identify characteristic wind turbine effects in the time-frequency
domain.
• Several examples have been updated to illustrate to new capabilities

• The Simulating Radar Systems with Atmospheric Refraction example now shows how to
simulate refraction using the radarDataGenerator System object. The original example only
demonstrated workflow using the radarTransceiver System object.
• The updated Simulating Polarimetric Radar Returns for Weather Observations example shows
how to simulate NEXRAD IQ using the polarimetric radarTransceiver. The example also
demonstrates the fidelity of a simulation against recommended standard deviation metrics.
• The updated FMCW Radar Altimeter Simulation radar altimeter example shows visualizations
using a helper globe viewer.
• The Simulate and Mitigate FMCW Interference Between Automotive Radars example now
shows how to mitigate interference in the spatial-domain using the new generalized likelihood
ratio test detector phased.GLRTDetector System object from the Phased Array System
Toolbox™.

Radar Toolbox Support Package for Texas Instruments mmWave Radar

Sensors: Acquire data from Texas Instruments mmWave radar sensors
Radar Toolbox Support Package for Texas Instruments mmWave Radar Sensors enables you to
connect to and acquire radar data from Texas Instruments mmWave radar sensors. The support
package provides detailed Hardware Setup screens that let you set up the device and acquire data.
You can use the mmWaveRadar object to read detections from the supported radar sensors.

You can also build applications with multi-object trackers based on the radar detections by using the
Sensor Fusion and Tracking Toolbox™ along with the support package. For more information, refer to
these application examples:

4-2
• People Tracking Using TI mmWave Radar
• Track Objects in a Parking Lot Using TI mmWave Radar

Radar Toolbox Support Package for Texas Instruments mmWave Radar

Sensors: Support for IWRL6432BOOST (October 2023, Version 23.2.1)
Radar Toolbox Support Package for Texas Instruments mmWave Radar Sensors now provides support
for acquiring radar data from Texas Instruments IWRL6432BOOST mmWave radar sensor also. The
Hardware Setup process now detects the connected IWRL6432BOOST board and lets you complete
the initial configuration. You can then specify "TI IWRL6432BOOST" as the value for boardname
argument while creating the mmWaveRadar object and configure other properties to read detections
and other measurements from the IWRL6432BOOST board.

4-3
 5

R2023a

Version: 1.4

New Features

Bug Fixes
R2023a

Improvements to surface scattering and clutter simulations

• The UseBeam property of the ClutterGenerator System object affects the behavior of the
radarDataGenerator and radarTransceiver System objects when generating clutter. The
UseBeam property now accurately accounts for the radarDataGenerator field of view. The
property also accounts for the radarTransceiver beam shape when it is used with a linear
array.
• This release provides an adaptive clutter generation scheme applicable to homogeneous surfaces
when using with the clutter generator clutterGenerator function in a radarScenario. This
scheme is incorporated using new and modified properties for the ClutterGenerator System
object. To enhance surface scattering modeling, the ScattererDistribution property defines
how to distribute scatterers on the surface. You can set the property to 'Uniform' or
'RangeDopplerCells'. When you select 'Uniform', the object distributes scatterers uniformly
over the surface according to the Resolution property. When you select
'RangeDopplerCells', the object distributes scatterers according to the radar range-Doppler
resolution cells. The 'RangeDopplerCells' option applies only to the radarTransceiver
radar model and non-height-mapped surfaces.
• Prior to this release, when terrain was specified for a land surface, or a sea surface with a spectral
model, each facet of the associated height map is equated to one clutter patch. This meant that
the effective "clutter resolution" for height-mapped surfaces was determined by the spacing of the
height map. With this change, height maps are now upsampled as needed so that patches are
spaced according to the Resolution property.
• You can use the new SeedSource and Seed properties to generate locations of random clutter. By
specifying the Seed property, you can run the exact same scenario multiple times with identical
randomized clutter.
• The ringClutterRegion function now returns a handle to a clutter region. This enables you to
modify ring clutter region features during a simulation.

Create radar blind zone maps

Multiple pulse repetition frequencies (PRFs) let you resolve radar range and velocity ambiguities and
also reject slow-moving targets and sidelobe clutter. However, selecting a set of PRFs is often difficult
because the choice depends on operational conditions such as radar altitude, speed, and clutter
levels. This release introduces blind zone maps to help radar designers select PRF sets. Three new
functions, applicable to monostatic pulse-Doppler radar, provide interfaces for visualizing radar blind
zones and ambiguities.

• The blindrangemap function creates blind range maps.

• The blindvelocitymap function creates blind velocity maps.
• The blindzonemap function creates range-velocity blind zone maps.

Polarization support for surfaces and targets

The surfaceReflectivityLand, surfaceReflectivitySea, surfaceReflectivityCustom,
and surfaceReflectivity System objects now enable reflection of polarized signals from land and
sea surfaces.

The rcsSignature function now enables reflection of polarized signals from targets. This function
creates a radar cross-section (RCS) signature object that models the interaction of platforms with

5-2
radio frequency (RF) emitters, signals, and sensors. You can use the new ShhPattern, SvvPattern,
ShvPattern, and SvvPattern properties to specify polarization scattering matrices.

Radar range limits for radarTransceiver System object

This release adds range limits for radar IQ data generation in the radarTransceiver object. These
limits enhance radar performance and represent real-world radar systems more faithfully. You can
implement the limits using four new properties. The RangeLimits property sets minimum and
maximum ranges of interest for the received radar signal. This property applies when you set the
RangeLimitsSource property to 'Property'. You can use the RangeOutputPort property to
enable range output and the TimeOutputPort property to enable time output.

Extended target support for radarTransceiver

The radarTransceiver object now generates target returns over the visible faces of an extended
target. An extended target has non-zero dimensions. You can define the dimensions of extended
targets by setting the Length, Width, and Height in the Dimensions property using the platform
function.

Radar scenario atmosphere models

In radar scenarios defined by a radarScenario object that employ a radarDataGenerator,
atmosphere models other than FreeSpace caused an error when calling the detect function. This
limitation has been removed. This enables the generation of radar detections taking into account
atmospheric refraction effects.

Automatic unit conversion in Radar Designer app

When you change units in the Radar Designer app, the values in some editing fields are now
automatically converted to equivalent values in the new units. These fields are located in the Radar,
Target, Environment, and Metrics and Requirements panels.

Applications: Surface clutter, Pulsed radar, MIMO radar

• The Simulate a Coastal Surveillance Radar example shows how to simulate a stationary land-
based radar to collect returns from extended targets at sea. You can configure sea surface
reflectivity models, modify a kinematic trajectory to match wave heights, and emulate a low-PRF
radar system in a radar scenario.
• The Simulate an Automotive 4D Imaging MIMO Radar example shows how to simulate a 4D
imaging MIMO radar for automotive applications. The example uses the radarTransceiver
object generating I/Q data producing a point cloud of signal-level radar detections.
• The Waveform Design and Signal Processing of Stepped Frequency Modulated Radar example
generates waveform level radar detections and range estimates for a stepped frequency
modulated (SFM) waveform. Estimate range and velocity based on simulated IQ data received in a
radar scenario using a radarTransceiver.
• In addition, three existing examples have been significantly updated with new sections on clutter
and automotive radar interference:

• Introduction to Radar Scenario Clutter Simulation

5-3
R2023a

• Maritime Clutter Suppression with Neural Networks

• Simulate FMCW Interference Between Automotive Radars

5-4
 6

R2022b

Version: 1.3

New Features

Bug Fixes
R2022b

Range-Doppler radar cross section

The new clutterSurfaceRangeDopplerRCS function generates radar cross sections (RCS) of flat
surfaces by decomposing the RCS into range and Doppler cells. Applicable to monostatic radar, the
function sums over many range-Doppler resolution cells to compute radar cross sections of extended
regions.

Scenario surface and clutter visualization

This release introduces two new plotting capabilities to Radar Toolbox for use with a theaterPlot
object. Both capabilities are implemented using a plotter configuration object, a plotting function, and
a data structure. You can plot land and sea surfaces and regions that generate clutter.

Scenario surface plotter

• Use the surfacePlotter object method to create a SurfacePlotter plotter object for
theaterPlot.
• Then use the plotSurface function to create a surface plot from surface data and the
SurfacePlotter object.
• Use the surfacePlotterData function to create a data structure to use as input to the
plotSurface function. The structure lets you plot surfaces managed by a SurfaceManager
object.

Scenario clutter plotter

• Use the clutterRegionPlotter object method to create a ClutterRegionPlotter plotter

object for theaterPlot.
• Then use the plotClutterRegion function to create a clutter plot from clutter data and the
ClutterRegionPlotter object.
• The clutterRegionData function creates a data structure that you can use as input to
plotClutterRegion function.

Atmospheric refracted signal paths

The new llarangeangle function enables you to compute the propagation range of a signal
between two points. Specify the points using latitude, longitude, and altitude. Additionally, you can
use the function to determine the angle of departure of the signal from the first point and the angle of
arrival at the second point. Because of atmospheric refraction, propagation paths follow a curved
trajectory. The refraction model uses the effective Earth radius approximation. You can specify the
effective Earth radius or use its default value of 4/3.

The slant2range function returns the refracted propagation range between a target and a sensor.
You can specify two different models of path refraction. Choose 'Curved' to use an effective earth
radius model for refraction or 'CRPL' to use an exponential reference atmosphere for refraction.

The new atmosphere object function models atmospheric refraction effects from within the
radarScenario object. The effearthradius function computes the effective Earth radius from an
atmosphere model.

6-2
New plot capabilities for Radar Designer app
Using the Radar Designer app, you can now generate Probability of Detection (Pd) versus signal-to-
noise ratio (SNR) plots. You can change radar parameters on the Radar, Target, or Environment
panels and view the changes on the plot. For example, you can change the Probability of false alarm
(Pfa) value on the Radar panel or change the Swerling target model on the Target panel.

In addition, you can now export Pd versus range plots, environmental loss plots, and link budget plots
by using the Export Detectability Analysis MATLAB script.

Radar budget plot

You can visualize a radar link budget as a waterfall chart using the radarbudgetplot function. The
waterfall chart shows the contributions of individual losses and gains present in a radar system to the
total energy required by the radar to produce a detection. The total gains and losses is called the
radar detectability factor.

Radar resource management: Workflow enhancements for

radarDataGenerator
This release introduces three changes to radarDataGenerator System object to facilitate radar
resource management (RRM) workflows.

There is now a custom scan mode for the object. Set the ScanMode property to 'Custom' and then
you can set the LookAngle property during a simulation. When you set the scan mode to 'Custom',
there is no distinction between electronic and mechanical scanning. In this mode, you can use only
monostatic workflows with no radar emissions are allowed. This scan mode disables the following
properties:

MaxAzimuthScanRate MaxElevationScanRate MechanicalAzimuthLimits

MechanicalElevationLimit ElectronicAzimuthLimits ElectronicElevationLimit
s s
MechanicalAngle ElectronicAngle FieldOfView
EmissionsInputPort

Several properties of the radarDataGenerator System object are now tunable. These property
modifications let you simulate RRM workflows that manage and dynamically change detection,
waveform, and antenna array parameters. You can tune the properties related to these parameters in
all scan modes.

CenterFrequency Bandwidth DetectionProbability

ReferenceRange ReferenceRCS FalseAlarmRate
RangeLimits RangeRateLimits MaxUnambiguousRange
MaxUnambiguousRadialSpee AzimuthResolution ElevationResolution
d
ElevationResolution RangeRateResolution

This release also adds a new BeamShape property to the radarDataGenerator when you use the
custom scan mode. You can specify BeamShape as 'Rectangular' or 'Gaussian'. Previously,

6-3
R2022b

when a radar beam scanned a target, the detection probability from the target remained the same
regardless of where the target was located within the beam. Now, when setting BeamShape to
'Gaussian', the detection probability is greatest when the target is centered on the beam and falls
off as the beam moves away from the target. Using BeamShape enables RRM workflows that optimize
beam placement, beam spacing, revisit times, and dwell times to improve the detection probability.

Use motion model name to obtain track position, velocity, and

covariance
By using the getTrackPositions and getTrackVelocities functions, you can now obtain
positions, velocities, and associated covariances of tracks by specifying the motion model name as an
input. For example,

[positions,covariances] = getTrackPositions(tracks,"constvel")

returns position and position covariances in tracks based on the constant velocity model defined in
the constvel function.

Previously, you could only use the position selector or velocity selector input to obtain the position
and velocity states. For example,

positionSelector = [1 0 0 0 0 0 0 0 0;
0 0 0 1 0 0 0 0 0;
0 0 0 0 0 0 1 0 0];
[positions,covariances] = getTrackPositions(tracks,positionSelector)

Confirm tracks in radarTracker

You can now confirm a track using the confirmTrack object function of the radarTracker System
object. The function confirms tracks for any specified TrackID.

Specify wait and reverse motion for waypoint trajectory

You can now specify wait and reverse motion using the waypointTrajectory System object.

• To let the trajectory wait at a specific waypoint, simply repeat the waypoint coordinate in two
consecutive rows when specifying the Waypoints property.
• To render reverse motion, separate positive (forward) and negative (backward) groundspeed
values by a zero value in the GroundSpeed property.

Applications: Radar models, clutter, and atmospheric effects

The release introduces several new application examples.

• Simulate a Maritime Radar PPI creates a plan position indicator (PPI) radar image for a rotating
antenna array in a maritime environment. You can configure a maritime radar scenario which
includes a spectral sea surface model and an extended target.
• The Maritime Clutter Removal with Neural Networks example shows how to train and evaluate a
convolutional neural network to remove clutter returns from maritime radar PPI images using the
Deep Learning Toolbox™.

6-4
• Multibeam Radar for Adaptive Search and Track shows how to use the radarDataGenerator in
a closed-loop simulation of a multifunction phased array radar (MPAR) that tracks multiple
maneuvering targets. The radar performs volume search, cued search, and tracking.
• FMCW Radar Altimeter Simulation illustrates how to model a radar altimeter and how to evaluate
its performance.
• Predict Surface Clutter Power in Range-Doppler Space calculates the radar cross section (RCS) of
surface clutter seen by a pulse-Doppler radar system.
• Simulating Radar Signals with Atmospheric Refraction Effects discusses environmental factors
that can result in signal loss and errors in target parameter estimation.
• The Simulate FMCW Interference Between Automotive Radars example shows how to simulate
interference between two FMCW automotive radars in a highway scenario.

6-5
 7

R2022a

Version: 1.2

New Features

Bug Fixes

Compatibility Considerations
R2022a

Land and sea surface modeling

This release gives you the capability to add land and sea surfaces to a radarScenario. Use the
landSurface object function to create a LandSurface object and the seaSurface object function
to create a SeaSurface object.

• The LandSurface object defines the terrain, boundary, reflectivity, and reference height of land
surfaces. The object enables you to create surfaces from imported DTED files. The
radarScenario object enables terrain occlusions when you use the detect object function of a
radarScenario object, or the detect object function of a Platform object in the scenario. If
the IsEarthCentered property of the radarScenario is set to true, the scenario models the
Earth surface using the WGS84 model.
• The SeaSurface object defines the boundary, sea surface motion, reflectivity, and reference
height of sea surfaces. You can use the seaSpectrum object to generate surface heights and
control the evolution of the surface over time. The object implements the Elfouhaily model for
creating an omnidirectional and wind-dependent spectrum. The object also allows the use to
specify a custom sea spectrum. The radarScenario object enables sea surface occlusions when
you use the detect object function of the scenario, or the detect object function of a Platform
object in the scenario. The spectral model is only available for non-Earth-centered scenarios.
• In this release, you can simulate clutter returns by using the clutterGenerator object function
of the radarScenario object. With clutterGenerator, you can control how clutter returns are
generated by the radarDataGenerator and radarTransceiver System objects. By default,
clutter returns are limited to the radar beam illumination area. Use the new
ringClutterRegion object function to define custom regions for the clutter generator.
• You can add multiple LandSurface objects and SeaSurface objects to a scenario. You generally
cannot not overlap LandSurface or SeaSurface objects. However, two surfaces can overlap as
long as at least one of them does not have height data. This means that for a LandSurface, the
Terrain property is empty and for a SeaSurface, the SpectralModel property is empty. Then
you can overlap a land surface with terrain or sea surface with a spectral model with an
unbounded flat background surface and place a land or a sea surface over it in some region. This
will generate clutter returns from background if the radar points outside the bounds of the
surface.
• You can manage land and sea surfaces using the SurfaceManager.

• You can determine if the line-of-sight between two positions in the scenario are occluded by
surfaces using the occlusion object function of the SurfaceManager object. To disable
surface occlusion, specify the UseOcclusion property of the SurfaceManager object as
false.
• You can obtain the height of land surfaces and sea surfaces in the scenario by using the
height object function of the SurfaceManager object.

Land and sea reflectivity

This release introduces four surface reflectivity objects and also updates the sea and land reflectivity
functions:

• The Radar Toolbox now has four new System objects designed to compute normalized reflectivity
when used within the radarScenario framework. These objects allow users to model reflectivity
for constant gamma, land, sea, and custom-specified surface.

7-2
 • surfaceReflectivity
• surfaceReflectivityLand
• surfaceReflectivitySea
• surfaceReflectivityCustom
• The landreflectivity function now supports seven land reflectivity models: Barton, APL,
Billingsley, GIT, Morchin, Nathanson, and Ulaby-Dobson.
• The seareflectivity function now supports nine sea reflectivity models: NRL, APL, GIT,
Hybrid, Masuko, Nathanson, RRE, Sittrop, and TSC.

RCS fluctuation modeling

This release enhances the radar cross-section model created by the rcsSignature class. The new
FluctuationModel property enables a statistical RCS fluctuation model that improves the fidelity of
the statistical radar model. The rcsSignature class is used by the radarDataGenerator when
simulating detection statistics.

Array scan loss

The new HasScanLoss property of the radarDataGenerator enables you to include off-broadside
losses that arise from electronic scanning. This property models the effect of antenna array beam
broadening when the radar points at an off-broadside angle. To enable this feature, set the
HasScanLoss property to true and set the ScanMode property to 'Custom'.

Wrap measurements in tracking filters to prevent filter divergence

You can enable measurement wrapping for these tracking filter objects:

• trackingEKF
• trackingUKF

First, specify the MeasurementWrapping property as true, and then specify the MeasurementFcn
property as a measurement function with two outputs: the measurement and the measurement
wrapping bound. With this setup, the filter wraps the measurement residuals according to the
measurement bounds, which helps prevent the filter from diverging due to incorrect measurement
residual values.

These measurement functions have predefined wrapping bounds:

• cvmeas
• cameas
• ctmeas

For these functions, the wrapping bounds are [–180, 180] degrees for azimuth angle measurements
and [–90, 90] degrees for elevation angle measurements. Other measurements are unwrapped.

You can also customize a wrapping-enabled measurement function by returning the wrapping bounds
as the second output of the measurement function.

7-3
R2022a

Changes to behavior of trackingKF object

Behavior change

As of R2022a, the trackingKF filter object has these behavior changes:

• The object now accepts and uses the process noise specified using the ProcessNoise property.
Previously, the object ignored the process noise specified in the ProcessNoise property.
• If you set the MotionModel property to a predefined state transition model, such as "1D
Constant Velocity", you can no longer specify the control model for the filter. To use a control
model, specify the MotionModel property as "Custom".
• To use a control model, you must:

• Specify the MotionModel property as "Custom" and use a customized motion model.
• Specify the control model when creating the filter.

Also, you cannot change the size of the control model matrix.
• You can no longer change the size of the measurement model matrix specified in the
MeasurementModel property.
• The dimension of the process matrix set through the ProcessNoise property now differentiates
between a predefined motion model and a customized motion model.

• If the specified motion model is a predefined motion model, specify the ProcessNoise
property as a D-by-D matrix, where D is the dimension of the motion. For example, D = 2 for
the "2D Constant Velocity" motion model.
• If the specified motion model is a customized motion model, specify the ProcessNoise
property as an N-by-N matrix, where N is the dimension of the state. For example, N = 6 if you
customize a 3-D motion model in which the state is (x, vx, y, vy, z, vz).
• The orientation of the state now matches that of the state vector that you specified when creating
the filter. For example, if you set the initial state in the filter as a row vector, the filter displays the
filter state as a row vector and outputs the state as a row vector when using the predict or
correct object functions. Previously, the filter displayed and output the filter state as a column
vector regardless of the initial state.
• You can generate efficient C/C++ code without dynamic memory allocation for trackingKF.

Applications: Surface and clutter simulation, multifunction radar, SAR

image formation
The release introduces several new application examples.

• Introduction to Radar Scenario Clutter Simulation shows how to generate monostatic surface
clutter signals and detections in a radar scenario using radarDataGenerator and
radarTransceiver.
• The Simulating Radar Returns from Moving Sea Surfaces example simulates an X-band radar
system used on a fixed offshore platform for oceanographic studies of sea states.
• In the Simulated Land Scenes for Synthetic Aperture Radar Image Formation example, a synthetic
aperture radar (SAR) system uses platform motion to mimic a longer aperture to improve cross-
range resolution.

7-4
• Simulate Radar Detections of Surface Targets in Clutter simulates the detection of targets in
surface clutter including the effect of Doppler separation and line-of-sight occlusions on
detectability.
• Generate Clutter and Target Returns for MTI Radar shows how to generate surface clutter and
target returns in the simulation of moving target indicator (MTI) radar.
• The Quality-of-Service Optimization for Radar Resource Management example sets up a resource
management scheme for a multifunction phased array radar (MPAR) surveillance based on a
quality-of-service (QoS) optimization.
• The Design and Simulate an FMCW Long-Range Radar (LRR) example configures a
radarDataGenerator from a radar design exported from the Radar Designer app.
• The ERS SAR Raw Data Extraction And Image Formation example shows how to extract European
Remote Sensing (ERS) Synthetic Aperture Radar (SAR) system parameters and unfocused raw
data and then form a focused image from raw data using a range migration image formation
algorithm.
• Processing Radar Reflections Acquired with the Demorad Radar Sensor Platform demonstrates
how to process and visualize FMCW radar echoes acquired via the Demorad Radar Sensor
Platform with the Phased Array System Toolbox and Simulink®.

7-5
 8

R2021b

Version: 1.1

New Features

Bug Fixes
R2021b

Radar Designer App: Plot vertical coverage diagrams

Starting this release, the Radar Designer app can plot vertical coverage diagrams. Vertical coverage
diagrams, also known as range-height-angle charts or Blake charts, show the relationship between
the range to a target, the height of the target, and the initial elevation angle of the transmitted rays
for the sensor. This visualization enables users to understand the propagation characteristics of
electromagnetic waves through the Earth's atmosphere.

This release also introduces four functions related to Blake charts:

• range2height computes the target height based on the initial elevation angle at the radar, the
antenna height, and the propagated range.
• height2range computes the propagated range based on the target height, the antenna height,
and the elevation angle at the radar.
• height2grndrange computes the ground range based on the target height, the antenna height,
and the elevation angle at the radar.
• refractionexp computes the refraction exponent, which is the decay constant of the Cosmic
Ray Physics Laboratory (CRPL) exponential reference atmosphere model.

Synthetic Aperture Radar: Convert between ground range resolution

and slant range resolution
This release introduces the grnd2slantrngres and slant2grndrngres functions.

• grnd2slantrngres returns the slant range resolutions corresponding to a set of ground range
resolutions and a set of grazing angles.
• slant2grndrngres returns the ground range resolutions corresponding to a set of slant range
resolutions and a set of grazing angles.

Radar Data Generator block: Generate radar data in Simulink

This release introduces the Radar Data Generator block. The block implements a statistical radar
sensor model that generates synthetic data and provides the option to generate tracks, detections,
and clustered detections. Radar Data Generator maintains the properties of the
radarDataGenerator System object.

New custom scan mode for radarDataGenerator

Starting this release, the radarDataGenerator System object has a custom scan mode that enables
users to point the radar beam in a specific direction.

Merge detections into clustered detections using mergeDetections

Use the mergeDetections function to merge detections that share the same cluster labels into
clustered detections. By default, the function uses a Gaussian mixture merging algorithm, but you
can customize your own detection merging algorithm.

8-2
Generate more memory-efficient C/C++ code from tracking filters
These objects now support strict single-precision and static memory allocation code generation:

• trackingEKF
• trackingUKF

See the Extended Capabilities section on each object reference page for its code generation
limitations.

Applications: AI, SAR, Tracking, Radar Coverage, Environment Effects

The release introduces several new application examples:

• Hand Gesture Classification Using Radar Signals and Deep Learning (Deep Learning Toolbox)
shows how to classify ultra-wideband (UWB) impulse radar signal data using a multiple-input,
single-output convolutional neural network (CNN).
• Introduction to SAR Target Classification Using Deep Learning lets you create and train a simple
convolution neural network to classify SAR targets using deep learning.
• Automatic Target Recognition (ATR) in SAR Images shows how to train a Region-based
Convolutional Neural Networks (R-CNN) for target recognition in large scene Synthetic Aperture
Radar (SAR) images using Deep Learning Toolbox and Parallel Computing Toolbox.
• Lidar and Radar Fusion in an Urban Air Mobility Scenario shows how to simulate radar and lidar
data and how to use multi-object trackers to track various unmanned aerial vehicles (UAVs) in an
urban environment.
• Extended Target Tracking with Multipath Radar Reflections in Simulink shows how to model and
mitigate multipath radar reflections in a highway driving scenario in Simulink.
• Radar Vertical Coverage over Terrain shows how to visualize 3-D vertical radar coverage over
terrain in the presence of heavy clutter.
• Modeling Target Position Estimation Errors discusses some of the environmental factors that can
result in detection losses and errors in target parameter estimation.
• Introduction to Scanning and Processing Losses in Pulse Radar demonstrates how various
parameters influence the losses that must be included in the radar detectability factor when
evaluating the radar equation. These include losses caused by the pulse eclipsing effect, off-
broadside scanning with an electronic beam, and MTI processing, CFAR loss, and filter matching
loss.
• Introduction to Pulse Integration and Fluctuation Loss in Radar illustrates how to compute gains
for several pulse integration techniques. It also demonstrates computation of the losses due to the
target's RCS fluctuation.

8-3
 9

R2021a

Version: 1.0

New Features
R2021a

New Radar Toolbox

The new Radar Toolbox features a comprehensive set of algorithms and tools for designing,
simulating, analyzing, and testing multifunction radar systems.

Radar Designer App: Model radar gains and losses and assess
performance in different environments
The Radar Designer app is an interactive tool that assists engineers and system analysts with high-
level design and assessment of radar systems at the early stage of radar development. Using the app,
you can:

• Assess and compare multiple radar designs in a single session

• Add smart radar, environment, and target Radar Designer Configurations to jump-start your
analysis
• Incorporate environmental effects due to Earth's curvature, atmosphere, terrain, and precipitation
• Add custom target radar cross-sections, antenna/array models, and both range-independent and
range-dependent losses
• Export and save results, sessions, models, and plots to continue your analysis

Evaluate Radar Performance

Radar Toolbox gives you tools to evaluate the performance of radar systems. You can find these tools
in Radar Systems Engineering. Significant capabilities allow you to:

• Evaluate the radar received signal-to-noise ratio as a function of transmitted power and target
range (radar equation)
• Derive detection and tracking statistics
• Evaluate antenna and receiver gains and losses
• Compute attenuation losses due to atmospheric effects, clutter, and weather
• Compute signal processing gains and losses for synthetic aperture radars

Create Radar Scenarios

Use the Radar Toolbox to create realistic radar scenarios. Functions for creating radar scenarios can
be found in Scenario Generation. You can:

• Model platform motion and orientation based on waypoints and trajectories or by simulating
inertial navigation systems
• Use radarScenario and other functions to create realistic radar scenarios for airborne, ground-
based, and shipborne platforms and targets
• Employ plotting functions to visualize the evolution of the radar scenario over time

Simulate Radar Data

The toolbox helps you create simulated radar data using the functions in Data Synthesis. With these
functions you can:

9-2
• Simulate radar data at probabilistic or signal levels.
• Generate signal and track data and object detections
• Simulate signal data including effects of multipath propagation, clutter, and interference
• Simulate target echoes from simple geometric shapes or complex structures such as a walking
pedestrian or a moving bicyclist

Signal and Data Processing

The toolbox lets you perform signal processing operations on simulated radar data. See Signal and
Data Processing for a description of the signal and data processing functions. Among the capabilities
are:

• Perform matched filtering and stretch-processing, pulse compression, coherent and noncoherent
pulse integration
• Estimate target range, Doppler and angle
• Employ constant false alarm rate (CFAR) techniques to reduce false detections
• Cluster neighboring detections into single extended detections
• Create, delete, and manage tracks for multiple objects

Applications
Radar Applications

• Simulate Radar Ghosts due to Multipath Return

• Highway Vehicle Tracking with Multipath Radar Reflections
• Radar Signal Simulation and Processing for Automated Driving
• Track-to-Track Fusion for Automotive Safety Applications
• Adaptive Tracking of Maneuvering Targets with Managed Radar
• Labeling Radar Signals with Signal Labeler
• Spaceborne Synthetic Aperture Radar Performance Prediction
• Airborne SAR System Design
• Synthetic Aperture Radar System Simulation and Image formation

Radar System Engineering

• Radar Architecture: Part 1 – System components and requirements allocation

• Radar Architecture: Part 2 – Test automation and requirements traceability
• Radar Link Budget Analysis
• Modeling Radar Detectability Factors
• MTI Improvement Factor for a Land-Based Radar System
• Sea Clutter Simulation for a Maritime Radar System
• Introduction to Modeling the Propagation of Radar Signals
• Receiver Operating Characteristic to Tracker Operating Characteristic

9-3
R2021a

Scenario Generation

• Radar Scenario Tutorial

• Radar Performance Analysis Over Terrain

Data Synthesis

• Simulating a Scanning Radar

• Simulating Passive Radar Sensors and Radar Interferences
• Transitioning From Statistical to Physics Based Radar Models

9-4