低秩稀疏分解混响抑制与自适应卡尔曼滤波相结合的多ping运动目标检测方法

IF 3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Yubin Fu , Xiaochuan Ma , Yu Liu , Xintong Wu , Tianhang Ji , Xingyuan Pei
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引用次数: 0

摘要

传统的低秩稀疏度分解(LRSD)不能将运动目标从稀疏波动混响和干扰中分离出来。因此,目标经常被混响和干扰所掩盖。因此,本文创新性地结合自适应卡尔曼滤波和连通区域(LRSD- cr - amfkf),提出了混响和干扰抑制LRSD算法。该算法利用连通区域和自适应卡尔曼滤波获得连续运动目标轨迹,并将波动、混响和干扰分离开来。同时,自适应卡尔曼滤波对干扰引起的测量误差进行补偿,提高了算法的抗干扰能力,减小了目标估计误差。最后,通过实验数据对LRSD-CR-AMFKF算法进行了验证。与传统的卡尔曼滤波和LRSD相比,提高了稀疏系数,分离了混响和干扰,减小了估计误差,得到了清晰、精确的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low rank sparsity decomposition reverberation suppression combined with adaptive Kalman filtering method for detecting multi-ping moving target
The traditional low rank sparsity decomposition (LRSD) can not separate the moving target from the sparse fluctuation reverberation and interference. Thus, the target is frequently masked by reverberation and interference. Therefore, the reverberation and interference suppression LRSD algorithm innovatively combined with the adaptive Kalman filtering and connected region (LRSD-CR-AMFKF) is proposed in this paper. The algorithm utilizes the connected region and adaptive Kalman filtering obtaining the continuous moving target trajectory and separates from the fluctuation reverberation and interference. Meanwhile, the adaptive Kalman filtering compensates for the measurement error caused by the interference, which improves the anti-interference ability of the algorithm and reduces the target estimation error. Finally, the LRSD-CR-AMFKF algorithm is validated by the experimental data. Compared with the conventional Kalman filtering and the LRSD, the sparse coefficient is improved, the reverberation and interference are separated, the estimation error is decreased, and a clean and precise target is obtained.
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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