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Hybrid Bayesian fusion of range-based and sourceless location estimates under varying observability

This paper proposes a hybrid Bayesian approach to multi-sensor data fusion for 3D localization. The approach addresses the problem of fusing range-based and sourceless localization estimates under conditions of varying observability in the range-based sub-system. The proposed localization approach uses a mixture of Single Hypothesis Tracking filtering (SHT) (e.g. Kalman filter) and Sequential Monte Carlo (SMC) filtering to improve accuracy under conditions of varying observability. Under conditions of sufficient or no range measurements, a SHT approach is used. Under conditions of insufficient range measurements (i.e. 1 or 2 ranges), SMC filtering is used, since it more accurately models the distribution of real error in the estimated positions using Gaussian mixtures rather that a single Gaussian. The results show up to a 10% improvement in 3D position estimation as compared to a Single-Constraint-at-a-Time (SCAAT) approach and up to a 24% improvement compared to an Extended Kalman Filter approach for intermittent 3 second partial range occlusions when tracking human arm movements.