Sparse Recovery-Based Target Detection for LFMCW Radar: An Estimation-Before-Detection Strategy

To suppress the sidelobes of strong targets, conventional radar signal processing techniques typically use data windows, which results in large performance losses for the detection of weak targets. Sparse recovery (SR) algorithms can suppress sidelobes without using data windows. However, these algorithms still face challenges in target detection, such as dealing with grid mismatch and the difficulty in designing a constant false alarm rate (CFAR) detector. To solve the above problems, we propose an estimate-before-detection (EBD) algorithm for linear frequency-modulated continuous wave (LFMCW) radar. First, parameter estimation is implemented before target detection based on the original signal. Remarkably, it has been proved that the fast Fourier transform (FFT) algorithm can be used to perform estimation of target parameters efficiently. Second, leveraging the sparsity of the received signal, the Gaussian generalized approximate message-passing-based low-complexity sparse Bayesian learning (GGAMP-SBL) algorithm is used to reconstruct targets. Then, target detection is executed using the reconstruction results. Numerical results demonstrate that the proposed detector maintains the CFAR property and outperforms the conventional signal processing (CSP)-based algorithm.