Adaptive sparse fractional order fuzzy function clutter suppression and moving target detection method

Abstract

The invention relates to an adaptive sparse fractional order fuzzy function SFRAF clutter suppression and moving target detection method, and belongs to the technical field of radar signal processing.The method comprises the following steps: firstly, performing SFRAF processing on an input radar echo signal, and quickly determining an optimal transformation order through hierarchical iterative kurtosis search; secondly, performing SFRAF domain adaptive least mean square filtering LMS operation, including a filtering process and an adaptive iteration process, and outputting a result when an error of a filter reaches a steady state; and finally, comparing the SFRAF amplitude of the output signal as a detection statistic with a threshold to obtain a moving target detection result. Accordingto the method, the performance of the time domain LMS filtering algorithm is improved. Relatively high operation efficiency is ensured under the condition of large data volume. Moreover, the method isnot sensitive to the change of sparsity parameters, is high in clutter suppression capability, and still has good detection performance for a maneuvering target under the condition of a low signal-to-clutter ratio.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing. More specifically, the invention relates to an adaptive sparse fractional fuzzy function clutter suppression and moving target detection method, which can be used for radar maneuvering target detection under the background of clutter. Background technique [0002] The rapid and effective detection of low-observable maneuvering targets has become a worldwide problem in the field of radar technology. Affected by the complex environmental background and the complex motion characteristics of the target, the echo signal-to-noise ratio (SCR) of the maneuvering target is low, and the echo Doppler presents time-varying characteristics. Rocking and undulating motions easily lead to high-order phases of echoes, which increases the difficulty of radar detection. The classical moving target detection (MTD) method based on Fourier transform is only suitable for stationary echo signals, and it...

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Problems solved by technology

SFRFT and SFRAF have the following two deficiencies when applied to maneuvering target detection in the clutter background: On the one hand, SFRAF needs to preset the signal sparsity K, and in practical applications, the signal sparsity is often unknown Or it may change, which reduces the robustness of the algorithm; on the other hand, SFRAF itself has no clutter suppression capability. In the case of low SCR, the reliability of signal reconstruction is poor, and the detection performance of the algorithm will be significantly reduced.

However, the time-domain LMS adaptive algorithm is more sensitive to the degree of dispersion of the eigenvalues ​​of the signal autocorrelation matrix, while the degree of dispersion of the eigenvalues ​​of the non-stationary signal autocorrelation matrix is ​​relatively large, resulting in a decrease in the convergence performance of the algorithm and failing to achieve a good filtering effect

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