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Robust tracking of a target in a clutter environment is an important andchallenging task. In recent years, the nearest neighbor methods andprobabilistic data association filters were proposed. However, the performanceof these methods diminishes as the number of measurements increases. In thispaper, we propose a robust generative approach to effectively model multiplesensor measurements for tracking a moving target in an environment with highclutter. We assume a time-dependent number of measurements that include sensorobservations with unknown origin, some of which may only contain clutter withno additional information. We robustly and accurately estimate the trajectoryof the moving target in a high clutter environment with an unknown number ofclutters by employing Bayesian nonparametric modeling. In particular, we employa class of joint Bayesian nonparametric models to construct the joint priordistribution of target and clutter measurements such that the conditionaldistributions follow a Dirichlet process. The marginalized Dirichlet processprior of the target measurements is then used in a Bayesian tracker to estimatethe dynamically-varying target state. We show through experiments that thetracking performance and effectiveness of our proposed framework are increasedby suppressing high clutter measurements. In addition, we show that ourproposed method outperforms existing methods such as nearest neighbor andprobability data association filters.

Use of Bayesian Nonparametric methods for Estimating the Measurements in High Clutter