Abstract

The problem of tracking multiple targets with multiple sensors in the presence of interfering measurements is considered. A new hybrid bootstrap filter is proposed. The bootstrap filter is an approach where random samples are used to represent the target posterior distributions. By using this approach, we circumvent the usual problem of an exponentially increasing number of association hypotheses as well as allowing the use of any nonlinear/non-Gaussian system and/or measurement models. I. INTRODUCTION This paper concerns the problem of tracking multiple targets using the information from multiple sensors. The sensors produce measurements as a result of random noise, clutter, countermeasures and interference, in addition to those from the required targets. Hence, it is usually not possible to distinguish with certainty the origin of the sensor measurements. In the Bayesian approach to target tracking, the aim is to construct the probability density function (pdf) of the targets conditi...

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