A maximum likelihood approach to data association

1992100 citationsJournal Article

Authors

Daniel Avitzour · Elbit Systems (Israel)

Abstract

An approach is presented to data association (DA) problems for which measurements are independent from scan to scan. It is demonstrated that maximum likelihood (ML) estimation of target parameters may be efficiently implemented by an EM iterative scheme. The algorithm is applied to multitarget trajectory estimation of constant-velocity targets from passive (bearing-only) sensors.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

Topics & Keywords

Target Tracking and Data Fusion in Sensor Networks Neural Networks and Applications Structural Health Monitoring Techniques

Publication Details

Published in: IEEE Transactions on Aerospace and Electronic Systems

Volume 28, Issue 2, pp. 560-566

DOI: 10.1109/7.144581

Field-Weighted Citation Impact: 0.54

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A maximum likelihood approach to data association

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Abstract

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Publication Details