Use of Bayesian Nonparametric methods for Estimating the Measurements in
High Clutter
- URL: http://arxiv.org/abs/2012.09785v1
- Date: Mon, 30 Nov 2020 18:32:34 GMT
- Title: Use of Bayesian Nonparametric methods for Estimating the Measurements in
High Clutter
- Authors: Bahman Moraffah, Christ Richmond, Raha Moraffah, and Antonia
Papandreou-Suppappola
- Abstract summary: We propose a robust generative approach to model multiple sensor measurements for tracking a moving target in a high clutter environment.
We employ a class of joint Bayesian nonparametric models to construct the joint prior distribution of target and clutter measurements.
We show through experiments that the tracking performance and effectiveness of our proposed framework are increased by suppressing high clutter measurements.
- Score: 4.547548797433131
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Robust tracking of a target in a clutter environment is an important and
challenging task. In recent years, the nearest neighbor methods and
probabilistic data association filters were proposed. However, the performance
of these methods diminishes as the number of measurements increases. In this
paper, we propose a robust generative approach to effectively model multiple
sensor measurements for tracking a moving target in an environment with high
clutter. We assume a time-dependent number of measurements that include sensor
observations with unknown origin, some of which may only contain clutter with
no additional information. We robustly and accurately estimate the trajectory
of the moving target in a high clutter environment with an unknown number of
clutters by employing Bayesian nonparametric modeling. In particular, we employ
a class of joint Bayesian nonparametric models to construct the joint prior
distribution of target and clutter measurements such that the conditional
distributions follow a Dirichlet process. The marginalized Dirichlet process
prior of the target measurements is then used in a Bayesian tracker to estimate
the dynamically-varying target state. We show through experiments that the
tracking performance and effectiveness of our proposed framework are increased
by suppressing high clutter measurements. In addition, we show that our
proposed method outperforms existing methods such as nearest neighbor and
probability data association filters.
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