# Distributed Multi-sensor Multi-view Fusion based on Generalized Covariance Intersection

## TL;DR

This paper introduces a distributed multi-sensor fusion method for multi-target tracking that combines Generalized Covariance Intersection with clustering to improve robustness and efficiency in sensors with different fields of view.

## Contribution

It proposes a novel fusion algorithm integrating GCI with clustering to handle multi-view sensor data and reduce false targets, with detailed GM implementation and analysis.

## Key findings

- Effective fusion of multi-view sensor data demonstrated
- Reduces false target detections compared to existing methods
- Robustness confirmed through numerical experiments

## Abstract

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.