A new sensing matrix construction strategy based on sparse recovery STAP

Li, Shuang; Yan, Wei; Zhou, Xibo; Ding, Mingkai

doi:10.1109/apsar46974.2019.9048498

Cited by 1 publication

(2 citation statements)

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“…In fact, based on the prior knowledge of clutter ridge, we know that the most atoms of has a little effect in performance, so whether there exists a way that reduce the number of atoms in while keeping the sparsity of signal. Generally speaking, it needs add more grids in clutter area and less grids in noise area as in [16]. However when the noise girds reduce to zero [15], it will lead to a poor recovery due to the decreasing of sparsity.…”

Section: The Contribution Of Different Type Gridsmentioning

confidence: 99%

“…RD method transforms the global optimization problem to local optimization with RD techniques such as beam-space post-Doppler [14], which cuts down the dimension of variables and has a significant computational savings. Spectrum information can also be used to design a more suitable dictionary [15,16], this way is easy to achieve and reduces iterating time significantly, however, a relatively small number of atoms may destroy the stability of algorithms [17,18].…”

Section: Introductionmentioning

confidence: 99%

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A novel reduced-dimensional dictionary grid screening strategy for SR-STAP

Cui

Xing

2020

SN Appl. Sci.

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The traditional space-time adaptive processing (STAP) methods require large training samples to estimate the clutter covariance matrix. Sparse recovery STAP (SR-STAP) can get a highly accurate estimation in the case of insufficient samples and it alleviates the above problem of conventional STAP methods, however, the price of SR-STAP is that it involves a huge computational load in optimization, especially when the input data size is very big. This paper proposed a novel SR-STAP dictionary construction method that can significantly improve computational efficiency. We introduce the noise grids as the supplement of the spectrum-aid methods and design a second grids screening method based on the prior spectrum information. After the process of the proposed method, the dimension of the space-time dictionary has a sharp decrease compared to the over-complete dictionary, moreover, the sparsity of variable can be maintained better than the common spectrum-aid methods. The numerical experiments verify that the proposed method not only can significantly reduce the optimization time to far smaller than the SR-STAP but also more robust than spectrum-aid methods even in the small input size.

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Section: The Contribution Of Different Type Gridsmentioning

confidence: 99%