Automotive FMCW Radar Development and Verification Methods

Abstract

This report describes the research on radar development and virtual verification methods using an automotive frequency-modulated continuous-wave (FMCW) mmWave sensing module. This thesis work provides a clear description on how a radar works from the analog front-end to target characterization through digital signal processing (DSP), by providing accurate and efficient verification methods to evaluate not only the radar performance but also the target characteristics. The report starts with a theoretical overview on FMCW radar concepts. The next part of report gives a detailed explanation on developing and setting up the hardware tool chain along with the software and tools that are necessary to capture the raw analog-to-digital converter (ADC) data. The next section of the report focuses on the main adopted implemented methodologies for the DSP block. Once all these different topics are covered, this project concludes by comparing the obtained results from the DSP algorithms with the ground-truth data obtained from different pre-defined hardware (HW) test-scenarios. The results from the thesis work mainly focus in three different type of analysis: stationary targets, dynamic targets and the comparison of implemented adaptive signal processing algorithm. For the first two type of analysis, results provide some evaluation criteria on fundamental radar performance parameters such as range, velocity and angle of arrival (AoA) estimation of the measured target with respect to radar. Additionally, the signal-to-noise ratio (SNR) is exploited to calculate one of the most relevant target characterization parameters: the radar cross section (RCS). For the last type of analysis, a success rate indicator compares and contrasts the performance of the proposed adaptive signal-processing algorithm with other similar implementations. The results from the analysis confirms the right implementation of a radar verification tool, which allows the user to extract the most relevant characteristics of the radar and target, with the freedom of changing the main algorithmic parameters and observe their effect on target detection and characterization. From the results, is clear that future improvements are needed to improve target detection algorithms especially for the cases involving certain corner scenarios.