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Double-leveled block sparsity-based space-time adaptive processing radar clutter suppression method

A space-time self-adaptive and clutter suppression technology, applied in the radar field, can solve the problems of performance degradation of the STAP method and achieve good low-speed target detection performance and good clutter suppression performance

Pending Publication Date: 2019-02-12
TSINGHUA UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to overcome the problem of performance degradation of the existing STAP method in the case of small samples, and propose a space-time automatic algorithm based on double-level block sparsity. Adaptive Processing Radar Clutter Suppression Method

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Embodiment Construction

[0032] The present invention proposes a space-time adaptive processing radar clutter suppression method based on dual-level block sparsity, which will be further described in detail below in conjunction with the accompanying drawings and specific implementation examples.

[0033] The present invention proposes a space-time adaptive processing radar clutter suppression method based on dual-level block sparsity, figure 1 It is a schematic diagram of the space-time adaptive processing scenario of the present invention. In the figure, it is assumed that the carrier aircraft flies along the x-axis at speed v, H represents the height of the carrier aircraft, and the circle represents the position of the uniform linear array antenna on board. The radar echo pulse sent by the airborne radar is sampled to obtain the echo data corresponding to each range unit. Such as figure 1 As shown, within each range cell, the clutter can be modeled as N c The sum of clutter blocks, triangle S k...

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Abstract

The invention proposes a double-leveled block sparsity-based space-time adaptive processing radar clutter suppression method, and belongs to the technical field of radar. According to the method, pulse compression and vectorization processing are performed on data acquired by onboard radar within a coherent processing interval, and data of a plurality of distance units around a distance unit to bedetected is taken as a training sample. With the method, the clustering characteristic of clutters in a space-time plane and the combined sparsity characteristic of an adjacent distance unit are combined, the cluster spectrum estimation accuracy is improved by double-leveled block sparsity, so that more accurate cluster covariance matrix estimation is obtained, and weight of a space-time adaptiveprocessing filter is obtained by calculation, and cluster suppression of radar data of the distance unit is achieved through processing the data of the distance unit to be detected by the space-timeadaptive processing filter. By the method, better cluster suppression performance and low-speed target detection performance can be obtained under the condition of a few training samples, and high application value is achieved.

Description

technical field [0001] The invention belongs to the technical field of radar, and in particular relates to a space-time adaptive processing radar clutter suppression method based on double-level block sparsity. Background technique [0002] Airborne radar has been widely used to detect moving targets on the ground and at sea. However, in addition to the target information, the radar echo also contains a large number of strong echoes from the ground or sea, also known as clutter. The target signal is often submerged in the clutter. , making the target undetectable to the airborne radar. In order to improve the target detection performance of airborne radar, it is necessary to suppress the clutter part of the received signal. Space-time adaptive processing (STAP) technology is an efficient means of clutter suppression. In order to ensure the clutter suppression effect of space-time adaptive processing, it is necessary to effectively estimate the covariance matrix of the clutt...

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Application Information

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IPC IPC(8): G01S7/36G01S7/41
CPCG01S7/36G01S7/414
Inventor 李刚姜智卓何友
Owner TSINGHUA UNIV
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