Izvorni znanstveni članak
https://doi.org/10.7305/automatika.2014.09.847
Bayesian Sensor Fusion Methods for Dynamic Object Tracking - A Comparative Study
Ivan Marković
orcid.org/0000-0003-4138-1113
; Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000, Zagreb, Croatia
Ivan Petrović
orcid.org/0000-0001-9961-5627
; Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000, Zagreb, Croatia
Sažetak
In this paper we study the problem of Bayesian sensor fusion for dynamic object tracking. The prospects of utilizing measurements from several sensors to infer about a system state are manyfold and they range from increased estimate accuracy to more reliable and robust estimates. Sensor measurements may be combined, or fused, at a variety of levels; from the raw data level to a state vector level, or at the decision level. In this paper we mainly focus on the Bayesian fusion at the likelihood and state vector level. We analyze two groups of data fusion methods: centralised independent likelihood fusion, where the sensors report only its measurement to the fusion center, and hierarchical fusion, where each sensor runs its own local estimate which is then communicated to the fusion center along with the corresponding uncertainty. We compare the prospects of utilizing both approaches, and present explicit solutions in the forms of extended information filter, unscented information filter, and particle filter. Furthermore, we also propose a solution for fusion of arbitrary filters and test it on a hierarchical fusion example of two of the aforementioned filters. Hence, the main contributions of this paper are systematic comparative study of Bayesian fusion methods, and a method for hierarchical fusion of arbitrary filters. The fusion methods are tested on a synthetic data experiment of tracking a dynamic object with several sensors of different accuracies by analyzing the quadratic Rényi entropy and root-mean-square error.
Ključne riječi
Bayesian sensor fusion; Information filter; Particle filter; Rényi entropy
Hrčak ID:
133187
URI
Datum izdavanja:
12.1.2015.
Posjeta: 1.747 *