Complementary waveforms for range sidelobe suppression based on a singular value decomposition approach

IF 1.1 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

IET Signal Processing Pub Date : 2023-05-04 DOI:10.1049/sil2.12218

Jiahuan Wang, Pingzhi Fan, Des McLernon, Zhiguo Ding

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Abstract

While Doppler resilient complementary waveforms (DRCWs) have previously been considered to suppress range sidelobes within a Doppler interval of interest in radar systems, their ability to provide Doppler resilience can be further improved. A new singular value decomposition (SVD)-based DRCW construction is proposed, in which both transmit pulse trains (made up of complementary pairs) and receive pulse weights are jointly considered. Besides, using the proposed SVD-based method, a theoretical bound is derived for the range sidelobes within the Doppler interval of interest. Moreover, based on the SVD solutions, a challenging non-convex optimization problem is formulated and solved to maximise the signal-to-noise ratio (SNR) with the constraint of low range sidelobes. It is shown that, compared with existing DRCWs, the proposed SVD-based DRCW has better Doppler resilience. Further, the new optimised SVD-based DRCW has a higher SNR while maintaining the same Doppler resilience.

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基于奇异值分解方法的距离旁瓣抑制互补波形

虽然多普勒弹性互补波形(DRCWs)以前被认为可以抑制雷达系统中感兴趣的多普勒间隔内的距离旁瓣，但它们提供多普勒弹性的能力可以进一步提高。提出了一种基于奇异值分解(SVD)的DRCW结构，该结构同时考虑了发射脉冲串(由互补对组成)和接收脉冲权值。此外，利用所提出的基于奇异值分解的方法，推导了多普勒感兴趣区间内距离旁瓣的理论边界。此外，在SVD解的基础上，提出了一个具有挑战性的非凸优化问题，并在低量程旁瓣的约束下实现了信噪比的最大化。实验结果表明，与现有DRCW相比，基于奇异值分解的DRCW具有更好的多普勒恢复能力。此外，新优化的基于svd的DRCW具有更高的信噪比，同时保持相同的多普勒弹性。

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来源期刊

IET Signal Processing 工程技术-工程：电子与电气

CiteScore

3.80

自引率

5.90%

发文量

审稿时长

9.5 months

期刊介绍： IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf