Multistatic Localization of a Moving Object Aided by Calibration Objects: Analysis and Solutions

IF 5.5 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-08-21 DOI:10.1109/TSP.2026.3726231
Danyan Lin;Gang Wang;K. C. Ho
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引用次数: 0

Abstract

It often occurs in multistatic localization systems that the direct paths between an unknown position transmitter and the receivers are absent or blocked. Moreover, the receivers may not be able to time-synchronize with one another. To enable the effective localization of a moving object, we introduce calibration objects to generate additional measurements that can help remove the receiver-dependent synchronization offsets and provide information about the transmitter position. First, we analyze the localizability of the object position and velocity, as well as the transmitter position, in relation to the number of calibration objects. Based on the localizability analysis, we formulate two semidefinite programming problems and develop a closed-form solution, and utilize their combinations to effectively solve the localization problems with different numbers of calibration objects. Additionally, the theoretical mean squared errors (MSEs) of these localization solutions are derived, demonstrating that they can achieve the Cramér-Rao lower bound (CRLB) performance. Finally, simulations validate both the theoretical findings and the good performance of the proposed solutions.
基于标定对象的运动目标多静态定位:分析与解决方案
在多静态定位系统中,经常出现未知位置发射机和接收机之间的直接路径缺失或阻塞的情况。此外,接收器可能无法彼此进行时间同步。为了实现移动物体的有效定位,我们引入了校准对象来生成额外的测量,这些测量可以帮助消除依赖于接收器的同步偏移,并提供有关发射器位置的信息。首先,我们分析了目标位置和速度的可定位性,以及发射机位置与校准目标数量的关系。在可定位性分析的基础上,提出了两个半定规划问题并给出了封闭解,并利用它们的组合有效地解决了不同数量标定对象的定位问题。此外,推导了这些定位方案的理论均方误差(MSEs),证明了它们可以达到cram - rao下界(CRLB)性能。最后,通过仿真验证了理论结果和所提方案的良好性能。
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来源期刊
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing 工程技术-工程:电子与电气
CiteScore
11.20
自引率
9.30%
发文量
310
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.
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