SPARSITY-AWARE RANGE AND VELOCITY ESTIMATION OF POINT TARGETS USING FMCW RADAR

Abstract

This thesis presents the implementation details and performance analysis of range and velocity estimation of point targets using a Frequency Modulated ContinuousWave (FMCW) radar, employing a novel combination of the 2D Discrete Fourier Transform (DFT) and the Orthogonal Matching Pursuit (OMP), a well-known sparse recovery algorithm. FMCW radars are widely used in various applications, including automotive radar systems, due to their ability to simultaneously measure both the distance and speed of point targets accurately. The 2D DFT is utilized to transform the acquired signal from the time domain to the frequency domain, enabling the extraction of range and Doppler frequency information. However, the conventional 2D-DFT method often suffers from potential issues related to resolution and noise. To address these challenges, the OMP algorithm is integrated to enhance the resolution and accuracy of the estimated parameters. OMP, a sparse signal recovery technique, effectively identifies the most significant components of the signal, thereby improving the precision of range and velocity estimates. The thesis details the theoretical background, implementation steps, and performance evaluation of the combined 2D DFT and OMP approach. Simulation results demonstrate the efficacy of this hybrid method in accurately estimating the range and velocity of multiple targets, showcasing its potential for advanced radar applications.