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