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

Radar maneuvering target detection in clutter background should not only consider the complex characteristics of the target to accumulate its energy as much as possible, but also suppress clutter to improve the signal-to-clutter ratio (SCR). The traditional fractional domain transform-based detection method requires parameters match searching, which costs heavy computational burden in case of a large amount of data. Sparse FT and sparse fractional FT can obtain high-resolution sparse representation of the target, but the signal sparsity needs to be known before, and the sparse representation performance is poor in clutter background. In this article, adaptive filtering method is introduced into the sparse fractional ambiguity function (SFRAF) method, and a SFRAF domain adaptive clutter suppression and highly maneuvering target detection algorithm is proposed, which is named as adaptive SFRAF (ASFRAF). The ASFRAF domain iterative filtering operation can suppress the clutter while retaining the signal energy as much as possible. Simulation results and measured radar data processing results show that the proposed algorithm can overcome the limitation of the SFRAF on the sparsity preset value and achieve high efficiency and robust detection of high-order phase maneuvering targets under a low SCR environment.

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