In this paper, the problem of tracking multiple targets in unknown clutter background using the Joint Integrated
Probabilistic Data Association (JIPDA) tracker and the Multiple Hypotheses Tracker (MHT) is studied. It is
common in real tracking problems to have little or no prior information on clutter background. Furthermore, the
clutter backgroundmay be dynamic and evolve with time. Thus, in order to get accurate tracking results, trackers
need to estimate parameters of clutter background in each sampling instant and use the estimate to improve
tracking. In this paper, incorporated with the JIPDA tracker or the MHT algorithm, a method based on Nonhomogeneous
Poisson point processes is proposed to estimate the intensity function of non-homogeneous clutter
background. In the proposed method, an approximated Bayesian estimate for the intensity of non-homogeneous
clutter is updated iteratively through the Normal-Wishart Mixture Probability Hypothesis Density (PHD) filter
technique. Then, the above clutter density estimate is used in the JIPDA algorithm and the MHT algorithm for
multitarget tracking. It is demonstrated thorough simulations that the proposed clutter background estimation
method improves the performance of the JIPDA tracker in unknown clutter background.
In this paper, methods of tracking multiple targets in non-homogeneous clutter background is studied. In many
scenarios, after detection process, measurement points provided by the sensor (e.g., sonar, infrared sensor, radar)
are not distributed uniformly in the surveillance region. On the other hand, in order to obtain accurate results,
the target tracking filter requires information about clutter's spatial density. Thus, non-homogeneous clutter
point spatial density has to be estimated based on the measurement point set and tracking filter's outputs. Also,
due to the requirement of compatibility, it is desirable for this estimation method to be integrated into current
tracking filters. In this paper, a recursive maximum likelihood method and an approximated Bayesian method
are proposed to estimate the clutter point spatial density in non-homogeneous clutter background and both will
in turn be integrated into Probability Hypothesis Density (PHD) filter. Here, non-homogeneous Poisson point
processes, whose intensity function are assumed to be mixtures of Gaussian functions, are used to model clutter
points. The mean and covariance of each Gaussian function is estimated and used in the update equation of the
PHD filter. Simulation results show that the proposed methods are able to estimate the clutter point spatial
density and improve the performance of PHD filter over non-homogeneous clutter background.
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