This illustration exemplifies the utilization of ray tracing to simulate the response of an FMCW radar to a predefined 3D scene, employing the powerful framework of RadarSimPy. Furthermore, it offers a comprehensive demonstration of fundamental range and Doppler processing techniques, enabling the extraction of crucial target information such as range and velocity.
This illustration provides a simulation of an FMCW radar system with a rotating metal plate. This simulation is executed through the raytracing framework available in RadarSimPy.
This illustration offers a simulation of an FMCW radar employing a trihedral corner reflector, implemented through the raytracing framework provided by RadarSimPy. Furthermore, it presents a practical demonstration of essential range and Doppler processing techniques, allowing the extraction of target range and velocity information, in addition to showcasing the two-dimensional CFAR technique.
RadarSimPy employs a combination of ray tracing and the PO approximation to simulate the RCS of a three-dimensional object based on its model. In this example, we illustrate how the RadarSimPy framework can be utilized to obtain the RCS of a car from various observation angles.
RadarSimPy integrates ray tracing and the PO approximation to model the RCS of three-dimensional objects. In this example, we showcase how the RadarSimPy framework can be used to calculate the RCS of a flat plate across various observation angles.
RadarSimPy combines ray tracing and the PO approximation to simulate the RCS of a three-dimensional object using its model. In this example, we demonstrate how the RadarSimPy framework can be applied to derive the RCS of a corner reflector across a range of observation angles and frequencies.
In the subsequent illustration, we will harness the potential of arbitrary waveform simulation in RadarSimPy to assess the impact of a non-linear chirp within the context of an FMCW radar scenario.
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.