基于全球导航卫星系统信号的无源雷达辐射源自适应选择方法研究

IF 0.5 Q4 ENGINEERING, MULTIDISCIPLINARY

Journal of Computational Methods in Sciences and Engineering Pub Date : 2023-12-15 DOI:10.3233/jcm227005

Huatao Tang, Hao Cha, Feng Liu, Bin Tian, Lei Zuo

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引用次数: 0

摘要

由于计算能力的限制，基于全球导航卫星系统信号的无源雷达可能无法使用所有的全球导航卫星系统信号，因此需要进行信号选择。基于 CRLB，提出了一种新的无源雷达辐射源自适应选择方法。以 GNSS 信号的 CRLB 最小化为目标，以 GNSS 信号数量为约束条件，建立了一个自适应选择模型。利用选择模型和粒子滤波算法进行了仿真实验。实验结果表明，该算法的计算精度略低于使用全部六颗卫星获得的跟踪结果，但计算时间大大缩短，降低了对雷达计算能力的要求。

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Research on adaptive selection method of radiation sources in passive radar based on GNSS signal

Due to limitations in computing power, the passive radar based on GNSS signal may not be able to use all the GNSS signals, requiring to make signal selection. Based on CRLB, a new radiation sources adaptive selection method for passive radar is proposed. With the goal of minimizing the CRLB of GNSS signals and the number of GNSS signals as constraints, an adaptive selection model is established. Simulation experiments were conducted using the selection model and particle filter algorithm. Experimental results show that the calculation accuracy of this algorithm is slightly lower than the tracking results obtained using all six satellites, but the calculation time is greatly reduced, reducing the requirement for radar computing power.

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来源期刊

Journal of Computational Methods in Sciences and Engineering ENGINEERING, MULTIDISCIPLINARY-

CiteScore

0.80

自引率

0.00%

发文量

152

期刊介绍： The major goal of the Journal of Computational Methods in Sciences and Engineering (JCMSE) is the publication of new research results on computational methods in sciences and engineering. Common experience had taught us that computational methods originally developed in a given basic science, e.g. physics, can be of paramount importance to other neighboring sciences, e.g. chemistry, as well as to engineering or technology and, in turn, to society as a whole. This undoubtedly beneficial practice of interdisciplinary interactions will be continuously and systematically encouraged by the JCMSE.