Work

Multi-target PHD filtering: proposition of extensions to the multi-sensor case

Year: 2010

Type: report

Abstract: Common difficulties in multi-target tracking arise from the fact that the system state and the collection of measures are unordered and their size evolve randomly through time. The random finite set theory provides a powerful framework to cope with these issues. This document focuses more particularly on the PHD (Probability Hypothesis Density) filter proposed by Mahler. The first part of this report is a synthesis of Mahler's work and aims at providing a thorough description of the construction of the single-sensor PHD filter. Then, based on a few leads provided by Mahler, the second part proposes several extensions of this filter to the multi-sensor case.

Source: HAL

Authors: Emmanuel Delande, Emmanuel Duflos, Dominique Heurguier, Philippe Vanheeghe

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2259604632

Cites:

Cited by: 13

Related to: 10