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基于贝叶斯估计的短波抗多径协同定位方法

Coordinated Positioning Method for Shortwave Anti-Multipath Based on Bayesian Estimation.

作者信息

Tang Tao, Jiang Linqiang, Zhao Paihang, Zheng Na-E

机构信息

Institute of Information Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China.

出版信息

Sensors (Basel). 2022 Sep 28;22(19):7379. doi: 10.3390/s22197379.

DOI:10.3390/s22197379
PMID:36236473
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9572003/
Abstract

Coordinated positioning based on direction of arrival (DOA)-time difference of arrival (TDOA) is a research area of great interest in beyond-visual-range target positioning with shortwave. The DOA estimation accuracy greatly affects the accuracy of coordinated positioning. With existing positioning methods, the elevation angle's estimation accuracy in multipath propagation decreases sharply. Accordingly, the positioning accuracy also decreases. In this paper, the elevation angle is modeled as a random variable, with its probability distribution reflecting the characteristics of multipath propagation. A new coordinated positioning method based on DOA-TDOA and Bayesian estimation with shortwave anti-multipath is proposed. First, a convolutional neural network is used to learn the three-dimensional spatial spectrogram to make an intelligent decision on the number of single and multiple paths, and to obtain a probability distribution of the elevation angle under multiple paths. Second, the elevation angle's estimated value is modified using the elevation angle's probability distribution. The modified elevation angle's estimated value is substituted into a DOA pseudo-linear observation equation, and the target position's estimated value is obtained using the matrix QR decomposition iteration algorithm. Finally, a TDOA pseudo-linear observation equation is established using the target estimate obtained in the DOA stage, and the coordinated positioning result is obtained using the matrix QR decomposition iteration algorithm again. Simulation results demonstrated that the proposed method had a stronger anti-multipath capability than traditional methods, and it improved the coordinated positioning accuracy of the DOA and TDOA. Measured data were used to validate the proposed method.

摘要

基于到达方向（DOA）-到达时间差（TDOA）的协同定位是短波超视距目标定位中备受关注的研究领域。DOA估计精度对协同定位精度有很大影响。在现有定位方法中，多径传播下仰角的估计精度会急剧下降。相应地，定位精度也会降低。本文将仰角建模为一个随机变量，其概率分布反映多径传播的特性。提出了一种基于DOA-TDOA和贝叶斯估计的短波抗多径协同定位新方法。首先，利用卷积神经网络学习三维空间频谱图，对单径和多径数量进行智能决策，并获得多径下仰角的概率分布。其次，利用仰角概率分布修正仰角估计值。将修正后的仰角估计值代入DOA伪线性观测方程，采用矩阵QR分解迭代算法获得目标位置估计值。最后，利用DOA阶段得到的目标估计值建立TDOA伪线性观测方程，再次采用矩阵QR分解迭代算法获得协同定位结果。仿真结果表明，该方法比传统方法具有更强的抗多径能力，提高了DOA和TDOA的协同定位精度。利用实测数据对所提方法进行了验证。

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