An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion

Abstract EN

In order to improve the detection and tracking performance of multiple targets from IR multispectral image sequences, the approach based on spectral fusion algorithm and adaptive probability hypothesis density (PHD) filter is proposed.

Firstly, the nonstationary adaptive suppression method is proposed to remove the background clutter.

Based on the multispectral image sequence, the spectral fusion method is used to detect the abnormal targets.

Spectral fusion produces the appropriate binary detection model and the computational probability of detection.

Secondly, the particle filtering-based adaptive PHD algorithm is developed to detect and track multiple targets.

This algorithm can deal with the nonlinear measurement on target state.

In addition, the calculated probability of detection substitutes the fixed detection probability in PHD filter.

Finally, the synthetic data sets based on various actual background images were utilized to validate the effectiveness of the detection approach.

The results demonstrate that the proposed approach outperforms the conventional sequential PHD filtering in terms of detection and tracking performances.