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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. <\/jats:p>","DOI":"10.1177\/15501477211030121","type":"journal-article","created":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T11:27:46Z","timestamp":1625657266000},"page":"155014772110301","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["A balanced sensor scheduling for multitarget localization in a distributed multiple-input multiple-output radar network"],"prefix":"10.1177","volume":"17","author":[{"given":"Chenggang","family":"Wang","sequence":"first","affiliation":[{"name":"Southwest China Institute of Electronic Technology, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0424-3010","authenticated-orcid":false,"given":"Zengfu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern 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