Multitarget Sensor Resolution Model and Joint Probabilistic Data Association
Journal article, 2012

In the design of target tracking algorithms, the aspect of sensor resolution is rarely considered. Instead, it is usually assumed that all targets are always resolved, and that the only uncertainties in the data association are which targets that are detected, and which measurement each detected target gave rise to. However, in situations where the targets are closely spaced in relation to the sensor resolution, this assumption is not valid, and may lead to degraded tracking performance due to an incorrect description of the data. We present a framework for handling sensor resolution effects for an arbitrary, but known, number of targets. We propose a complete multitarget sensor resolution model that can be incorporated into traditional Bayesian tracking filters. Further, the exact form of the posterior probability density function (pdf) is derived, and two alternative ways of approximating that exact posterior density with a joint probabilistic data association (JPDA) filter are proposed. Evaluations of the resulting filters on simulated radar data show significantly increased tracking performance compared with the JPDA filter without a resolution model.

Data models

Remote sensing

Target tracking

Radar tracking

Sensor resolution


Daniel Svensson

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Martin Ulmke

Fraunhofer-Institut fur Kommunikation, Informationsverarbeitung und Ergonomie

Lars Hammarstrand

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

IEEE Transactions on Aerospace and Electronic Systems

0018-9251 (ISSN) 15579603 (eISSN)

Vol. 48 4 3418-3434 6324722

Areas of Advance

Information and Communication Technology



Basic sciences

Subject Categories

Probability Theory and Statistics

Signal Processing



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