Radar Moving Target Detection in Clutter Background via Adaptive Dual-Threshold Sparse Fourier Transform

Abstract

In this paper, an adaptive dual-threshold sparse Fourier transform (ADT-SFT) algorithm is proposed, which enables the application of the SFT and robust SFT (RSFT) to the moving target detection in clutter background. Two levels of detection are introduced in this algorithm. First, a scalar constant false alarm rate (CFAR) detection is employed in each frequency channel formed by subsampled fast Fourier transform (FFT) to suppress the influence of strong clutter points on the sparsity and frequencies estimation. Second, the subspace detector constructed by suspected target Doppler frequencies is adopted to complete the target detection. The simulation analysis and results of the measured sea clutter data show that the ADT-SFT algorithm is more suitable for the clutter background and can obtain better detection performance than SFT and RSFT. In addition, compared with the conventional subspace detection (SD) algorithm, which needs to search all the Doppler frequencies one-by-one to establish the detector, the ADT-SFT algorithm only needs to search a small number of suspected target Doppler frequencies, and therefore, the computational complexity can be greatly reduced.

Keywords

• Moving target detection
• sparse Fourier transform (SFT)
• adaptive dual-threshold sparse Fourier transform (ADT-SFT)
• constant false alarm rate (CFAR) detection
• subspace detection (SD)