Reduced-dimension STAP method for conformal array based on sequential convex programming

IF 3.4 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Signal Processing Pub Date : 2024-11-01 DOI:10.1016/j.sigpro.2024.109745
Jingxi Shi , Xueqi Yao , Zhihang Wang , Ziyang Cheng , Lei Xie
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Abstract

Compared with the uniform arrays, the conformal array can effectively reduce the radar cross section and improve the utilization of the limited space in the aircraft. However, the special array structure aggravates the non-stationarity of the clutter, and the typical blocking matrix construction method in the space–time adaptive processing (STAP) is not appropriate any more. To address these issues, a reduced dimension STAP algorithm based on penalty sequential convex programming in the generalized sidelobe cancellation structure is proposed. The blocking matrix, channel selection vector and STAP weights can be optimized simultaneously in the algorithm framework. To tackle the resultant nonconvex problem, we formulate the original optimization function as a quasi-convex form and solve these parameters alternately within an iterative framework. Numerical simulations are provided to validate the proposed method and demonstrate its high performance.
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基于顺序凸编程的共形阵列降维 STAP 方法
与均匀阵列相比,共形阵列能有效减小雷达截面,提高飞机有限空间的利用率。然而,特殊的阵列结构加剧了杂波的非稳态性,时空自适应处理(STAP)中典型的阻塞矩阵构造方法已不再适用。为解决这些问题,本文提出了一种基于广义侧叶消除结构中惩罚顺序凸编程的降维 STAP 算法。阻塞矩阵、信道选择向量和 STAP 权重可在算法框架中同时优化。为了解决由此产生的非凸问题,我们将原始优化函数表述为准凸形式,并在迭代框架内交替解决这些参数。我们提供了数值模拟来验证所提出的方法,并证明了它的高性能。
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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