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Your search keyword '"Mahler, Ronald P. S."' showing total 66 results
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66 results on '"Mahler, Ronald P. S."'

35. Robust Multi-Bernoulli Filtering.

Author

Vo, Ba-Tuong, Vo, Ba-Ngu, Hoseinnezhad, Reza, and Mahler, Ronald P. S.

Abstract

In Bayesian multi-target filtering knowledge of parameters such as clutter intensity and detection probability profile are of critical importance. Significant mismatches in clutter and detection model parameters results in biased estimates. In this paper we propose a multi-target filtering solution that can accommodate non-linear target models and an unknown non-homogeneous clutter and detection profile. Our solution is based on the multi-target multi-Bernoulli filter that adaptively learns non-homogeneous clutter intensity and detection probability while filtering. [ABSTRACT FROM PUBLISHER]

Published
2013
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43. Closed-Form Solutions to Forward–Backward Smoothing.

Author

Vo, Ba-Ngu, Vo, Ba-Tuong, and Mahler, Ronald P. S.

Subjects
STATE-space methods, STATISTICAL smoothing, GAUSSIAN sums, SIMULATION methods & models, KALMAN filtering, BINOMIAL distribution
Abstract

We propose a closed-form Gaussian sum smoother and, more importantly, closed-form smoothing solutions for increasingly complex problems arising from practice, including tracking in clutter, joint detection and tracking (in clutter), and multiple target tracking (in clutter) via the probability hypothesis density. The solutions are based on the corresponding forward–backward smoothing recursions that involve forward propagation of the filtering densities, followed by backward propagation of the smoothed densities. The key to the exact solutions is the use of alternative forms of the backward propagations, together with standard Gaussian identities. Simulations are also presented to verify the proposed solutions. [ABSTRACT FROM PUBLISHER]

Published
2012
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44. CPHD Filtering With Unknown Clutter Rate and Detection Profile.

Author

Mahler, Ronald P. S., Vo, Ba-Tuong, and Vo, Ba-Ngu

Subjects
*RECURSIVE functions, *BAYESIAN analysis, *UNCERTAINTY (Information theory), *ELECTRIC filters, *GAUSSIAN processes, *SIMULATION methods & models, *MARKOV processes, *APPROXIMATION theory, *PARAMETER estimation
Abstract

In Bayesian multi-target filtering, we have to contend with two notable sources of uncertainty, clutter and detection. Knowledge of parameters such as clutter rate and detection profile are of critical importance in multi-target filters such as the probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters. Significant mismatches in clutter and detection model parameters result in biased estimates. In practice, these model parameters are often manually tuned or estimated offline from training data. In this paper we propose PHD/CPHD filters that can accommodate model mismatch in clutter rate and detection profile. In particular we devise versions of the PHD/CPHD filters that can adaptively learn the clutter rate and detection profile while filtering. Moreover, closed-form solutions to these filtering recursions are derived using Beta and Gaussian mixtures. Simulations are presented to verify the proposed solutions. [ABSTRACT FROM PUBLISHER]

Published
2011
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