Multi-sensor PHD: Construction and implementation by space partitioning

The Probability Hypothesis Density (PHD) is a well-known method for single-sensor multi-target tracking problems in a Bayesian framework, but the extension to the multi-sensor case seems to remain a challenge. In this paper, an extension of Mahler’s work to the multi-sensor case provides an expression of the true PHD multi-sensor data update equation. Then, based on the configuration of the sensors’ fields of view (FOVs), a joint partitioning of both the sensors and the state space provides an equivalent yet more practical expression of the data update equation, allowing a more effective implementation in specific FOV configurations.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Emmanuel Delande, Emmanuel Duflos, Philippe Vanheeghe, Dominique Heurguier
• Keywords: ICASSP 2011, Multi-sensor Case, PHD Multi-sensor Data, Signal Processing, Single-sensor Multi-target Tracking