Asymptotically Efficient Moving Target Localization in Distributed Radar Networks

IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Mohammad Reza Jabbari;Mohammad Reza Taban;Saeed Gazor;Mehrdad Kaimasi
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引用次数: 0

Abstract

In this article, we investigate the joint estimation of the position and velocity of a moving target in distributed networks of moving radars using Time Of Arrival (TOA) and Doppler Shift (DS) measurements. In contrast to most of the existing/recent methods, we avoid the use of Nuisance Variables (NVs) by employing algebraic manipulations. We reformulate a new set of equations that are linear with respect to the target's position and velocity, resulting in a significant performance improvement. Subsequently, we propose a Two-Stage Weighted Least Squares (TSWLS) estimator and recommend two alternative algorithms to reduce computational complexity while preserving the accuracy by selecting either a transmitter or receiver as the reference sensor. We implement the proposed method over fully and partially connected networks. Our theoretical derivations and numerical simulations reveal that the proposed estimators are asymptotically efficient, i.e., they attain the CRLB, at relatively high noise levels. Moreover, the simulation results show that the proposed methods outperform state-of-the-art algorithms.
分布式雷达网络中渐进有效的运动目标定位
在本文中,我们研究了使用到达时间(TOA)和多普勒频移(DS)测量对移动雷达分布式网络中移动目标的位置和速度的联合估计。与大多数现有/最新的方法相比,我们通过使用代数运算来避免使用干扰变量(NV)。我们重新制定了一组新的方程,这些方程相对于目标的位置和速度是线性的,从而显著提高了性能。随后,我们提出了一种两阶段加权最小二乘(TSWLS)估计器,并推荐了两种替代算法,通过选择发射机或接收机作为参考传感器来降低计算复杂度,同时保持精度。我们在完全和部分连接的网络上实现了所提出的方法。我们的理论推导和数值模拟表明,所提出的估计量是渐近有效的,即它们在相对较高的噪声水平下达到CRLB。此外,仿真结果表明,所提出的方法优于最先进的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Signal and Information Processing over Networks
IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications
CiteScore
5.80
自引率
12.50%
发文量
56
期刊介绍: The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.
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