A balanced sensor scheduling for multitarget localization in a distributed multiple-input multiple-output radar network. (July 2021)

• Record Type: Journal Article
• Authors: Wang, Chenggang; Wang, Zengfu; Xu, Xiong; Hao, Yuhang
• Is Part Of: International journal of distributed sensor networks. Volume 17:Number 7(2021)
• Publication Date: 2021-07
• Subjects: Multiple-input multiple-output radars -- submodular set -- minimum-variance estimation -- sensor scheduling; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/15501477211030121
• Languages: English
• ISSNs: 1550-1329

Abstract

In this article, we consider the problem of optimally selecting a subset of transmitters from a transmitter set available to a multiple-input and multiple-output radar network. The aim is to minimize the location estimation error of underlying targets under a power constraint. We formulate it as a minimum-variance estimation problem and show that the underlying variance reduction function is submodular. From the properties of submodularity, we present a balanced selection policy which minimizes the worst-case error value using a minimax strategy. A greedy algorithm with guaranteed performance with respect to optimal solutions is given to efficiently implement the scheduling policy. The effectiveness and the efficiency of the proposed algorithm are demonstrated in simulated examples.