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Message Passing Based Robust Target Localization in Distributed MIMO Radars in the Presence of Outliers

Zehua Yu, Jun Li, Qinghua Guo, Ting Sun

2020IEEE Signal Processing Letters39 citationsDOI

Abstract

In this letter, a novel factor graph approach to target localization in distributed MIMO radars is proposed. To achieve robust localization in the presence of outliers, target localization can be formulated as a least absolute deviation (LAD) problem, which, however, is difficult to solve. We then reformulate the LAD problem as a reweighted least square (LS) one, which is converted to a product of some functions, enabling the use of factor graph techniques. Based on a factor graph representation, a highly efficient message passing algorithm is developed, where the target location is estimated in an iterative way. Comparisons with state-of-the-art methods show that the proposed method is superior in terms of computational complexity, robustness and accuracy.

Topics & Concepts

Factor graphRobustness (evolution)OutlierComputer scienceMessage passingGraphAlgorithmMIMOIterative methodComputational complexity theoryLeast absolute deviationsRepresentation (politics)Artificial intelligenceMathematicsTheoretical computer scienceDecoding methodsChemistryStatisticsPoliticsGeneComputer networkProgramming languageChannel (broadcasting)BiochemistryEstimatorPolitical scienceLawRadar Systems and Signal ProcessingAdvanced SAR Imaging TechniquesDirection-of-Arrival Estimation Techniques