Application of probabilistic PCR5 Fusion Rule for Multisensor Target Tracking

TL;DR

This paper introduces a probabilistic PCR5 fusion rule for multisensor target tracking, demonstrating its robustness and ability to preserve multiple hypotheses in non-linear filtering scenarios.

Contribution

It develops a non-Bayesian fusion rule based on PCR5 within the DSmT framework, tailored for probabilistic densities in multisensor tracking.

Findings

p-PCR5 maintains multiple hypotheses after fusion.

It is more robust to modeling errors.

Effective in non-linear distributed filtering.

Abstract

This paper defines and implements a non-Bayesian fusion rule for combining densities of probabilities estimated by local (non-linear) filters for tracking a moving target by passive sensors. This rule is the restriction to a strict probabilistic paradigm of the recent and efficient Proportional Conflict Redistribution rule no 5 (PCR5) developed in the DSmT framework for fusing basic belief assignments. A sampling method for probabilistic PCR5 (p-PCR5) is defined. It is shown that p-PCR5 is more robust to an erroneous modeling and allows to keep the modes of local densities and preserve as much as possible the whole information inherent to each densities to combine. In particular, p-PCR5 is able of maintaining multiple hypotheses/modes after fusion, when the hypotheses are too distant in regards to their deviations. This new p-PCR5 rule has been tested on a simple example of distributed non-linear filtering application to show the interest of such approach for future developments. The non-linear distributed filter is implemented through a basic particles filtering technique. The results obtained in our simulations show the ability of this p-PCR5-based filter to track the target even when the models are not well consistent in regards to the initialization and real cinematic.