Generalising Covariance Intersection for multiple posterior densities in multi-sensor fusion
Murat Üney, Simon J. Julier, Simon Maskell · 2025
Covariance intersection (CI) is a widely studied method to combine posterior state probability densities in sensor fusion. CI combines two input densities by first considering their exponential mixture density (EMD), or weighted geometric mean density, followed by selecting the mixture weights. In this work, we first introduce Kullback-Leibler divergence centroid (KLDC) and minimum Entropy (ME) generalisations of CI to an arbitrary number of state densities. Then, we compare these two generalisations and the equally-weighted geometric average (EWGA) fusion, which is widely used in the literature, in scenarios with correlated measurements and clutter, or false alarms. Our experiments indicate that ME fusion performs the best in the case of highly correlated measurement errors or false alarms, whilst EWGA shows better performance in no to medium correlation with no false alarms.