Distributed Multi-sensor Multi-view Fusion based on Generalized Covariance Intersection
For multi-sensor tracking systems, this work addresses the challenge of fusing information from sensors with non-overlapping fields of view, improving robustness and reducing false targets.
The paper proposes a distributed fusion algorithm for multi-target tracking with sensors having different fields of view, combining Generalized Covariance Intersection with a clustering algorithm to fuse posterior PHDs. Numerical experiments confirm its effectiveness.
Distributed multi-target tracking (DMTT) is addressed for sensors having different fields of view (FoVs). The proposed approach is based on the idea of fusing the posterior Probability Hypotheses Densities (PHDs) generated by the sensors on the basis of the local measurements. An efficient and robust distributed fusion algorithm combining the Generalized Covariance Intersection (GCI) rule with a suitable Clustering Algorithm (CA) is proposed. The CA is used to decompose each posterior PHD into well-separated components (clusters). For the commonly detected targets, an efficient parallelized GCI fusion strategy is proposed and analyzed in terms of $L_1$ error. For the remaining targets, a suitable compensation strategy is adopted so as to counteract the GCI sensitivity to independent detections while reducing the occurrence of false targets. Detailed implementation steps using a Gaussian Mixture (GM) representation of the PHDs are provided. Numerical experiments clearly confirms the effectiveness of the proposed CA-GCI fusion algorithm.