基于gnss的无源双基地雷达距离-多普勒图的多弱目标探测与测量

IF 3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Zuhan Cheng , Jun Wang , Te Zhao , Jinxin Sui , Ziqian Huang , Hui Ma , Luo Zuo
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引用次数: 0

摘要

本文探讨了GNSS卫星系统作为无源雷达机动目标探测的照明。该技术的主要难点是导航卫星的到达功率有限。针对这一问题,提出了一种多弱目标检测的长时间积分算法。首先对信号模型进行分段,得到距离压缩数据,然后进行二次梯形变换,修正帧内二次距离偏移。然后,采用基于改进变分贝叶斯框架的多目标运动参数估计方法对方位信号进行估计。最后,采用适当的补偿策略对所有集成地图中的目标位置进行对齐。该方法对双基地距离-多普勒图中多目标的运动测量值进行了准确估计,并对复杂的距离和多普勒偏移进行了补偿,提高了探测能力。通过理论分析、仿真和实验验证了该技术的有效性。仿真和实测数据实验均证明了该方法比现有方法具有显著的多弱目标检测和测量性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-weak targets detection and measurement from range-Doppler maps in GNSS-based passive bistatic radar
This paper explores the GNSS satellite system as illumination in passive radar for maneuvering target detection. The main difficulty of this technology is the limited arrival power from navigation satellites. To address this, a long-time integration algorithm for multi-weak targets detection is proposed. It begins with the segmented signal model to obtain the range-compressed data, followed by the second-keystone transform to correct the intra-frame quadratic range migration. Then, a multi-target motion parameter estimation method based on the modified variational Bayesian framework is employed on the azimuth signal. Lastly, a proper compensation strategy is applied to align the targets’ positions in all integrated maps. It estimates the multiple targets’ motion measurements from the bistatic range-Doppler maps accurately, and compensates for the complicated range and Doppler migrations to improve the detection capability. This technique is validated through theoretical analysis with simulations, as well as experiments. Both simulated and real-measured data experiments prove the remarkable multi-weak targets detection and measurement performance than the existing methods.
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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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