An Investigation of Using Rigid Body Receivers for Locating a Non-Cooperative Object by Pseudo-Ranges in the Absence of Synchronization

IF 5.8 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Signal Processing Pub Date : 2025-01-27 DOI:10.1109/TSP.2025.3535099
Xiaochuan Ke;K. C. Ho
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Abstract

A traditional receiver has only one sensor to observe the signal from an object for localization. This research investigates the extension of a receiver to a rigid body (RB) that has several sensors attached to its different spots for non-cooperative localization. In addition to the position uncertainties of RB receivers as in a typical wireless sensing network, their orientations may not be known. We show that using RB receivers relieves the stringent requirement of synchronization among them for locating a non-cooperative object by time observations, even without knowledge about the orientations of the RBs. The minimum number of illuminators, RB receivers and sensors, as well as the geometry requirement to achieve localization are established, for the cases of without and with the availability of inaccurate orientations. The optimum placements of the sensors within an RB receiver and the RBs in the localization space are derived to achieve the A-optimality for the object location estimation under Gaussian noise. Simulation results confirm well the developed theories.
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非同步条件下刚体接收机伪距离定位非合作目标的研究
传统的接收器只有一个传感器来观察来自物体的信号并进行定位。本研究探讨了将接收器扩展到刚体(RB),该刚体有多个传感器附着在其不同的位置以进行非合作定位。除了典型无线传感网络中RB接收器的位置不确定性外,它们的方向可能是未知的。我们的研究表明,即使不知道RB的方向,使用RB接收器也可以通过时间观测减轻它们之间同步定位非合作目标的严格要求。在没有和有不准确定向的情况下,确定了照明器、RB接收器和传感器的最小数量,以及实现定位的几何要求。推导了传感器在RB接收机内的最佳位置和定位空间中的最佳位置,实现了高斯噪声下目标定位估计的a -最优性。仿真结果很好地证实了所提出的理论。
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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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