RadarSimPy offers support for simulating transmitter phase noise. By integrating measured phase noise data into the simulation module, users can assess how phase noise affects the performance of the radar system. Here’s an illustrative example of how to incorporate phase noise into the radar module.
This example introduces constant false alarm rate (CFAR) detection and shows how to use RadarSimPy to perform Cell-Averaging CFAR (CA-CFAR) detection and Ordered Statistics CFAR (OS-CFAR) detection.
The ray tracing engine within RadarSimPy can be harnessed to generate a point cloud within a user-defined scene. This point cloud primarily comprises the initial reflection points of the ray clusters, effectively resembling the point cloud obtained through Lidar technology.
RadarSimPy provides a suite of tools for analyzing receiver operational characteristics. Here, we present an example to illustrate the utilization of these analysis tools.
RadarSimPy offers support for various modulation schemes. This sample serves as a demonstration of how to utilize pulse modulation to construct a PMCW radar system, showcasing the fundamental signal processing techniques involved.
Herein is an illustrative instance of a Doppler radar simulation, utilizing the framework of RadarSimPy.
This illustrative example showcases the effective utilization of channel delay and prp (pulse repetition period) for configuring a TDM MIMO radar using RadarSimPy.
This illustration showcases a simulation of an FMCW radar using the RadarSimPy framework. It additionally provides a demonstration of fundamental range and Doppler processing techniques for extracting target range and velocity information.