LAUSR

Constant false alarm rate (CFAR) detection algorithms, which are widely used in frequency-modulated continuous wave (FMCW) radar systems, achieve target detection by employing a threshold determined on the basis of a predicted background level. However, in multitarget scenarios, the multitarget shadowing effect can lead to inaccurate prediction of the background level and improper setting of the threshold, which then results in severely degraded CFAR performance. To combat this multitarget shadowing effect, a novel CFAR algorithm based on sparsity adaptive correlation maximization (SACM-CFAR) is proposed in this work. The proposed SACM-CFAR algorithm realizes target detection by utilizing the correlation between linear measurements of the radar intermediate frequency (IF) signal and the sensing matrix. To achieve a desired false alarm rate, the proposed algorithm determines the threshold by estimating the distributed parameters of the reduced sample set obtained by removing the detected targets from the original sample set. Both simulation results and field test results verify that the proposed algorithm outperforms conventional algorithms in multitarget scenarios.