Abstract
This paper presents a parallel grid-based method and belief fusion for real-time cooperative Bayesian estimation. The grid-based recursive Bayesian estimation (RBE) method effectively maintains the belief of objects even with no detection event but requires large computation for its prediction and correction processes as well as fusion process in cooperative estimation. In order for real-time estimation, the belief fusion proposed in the paper carries out the fusion of belief outside the RBE loop. The parallelization of the entire grid-based method and belief fusion further accelerates the RBE so that real-time estimation is possible even in highly dynamical environments. Numerical examples have first demonstrated the validity of the proposed approach through parametric studies. The proposed approach was then applied to the cooperative search by autonomous unmanned ground vehicles (UGVs), and its real-time capability has been demonstrated.
Published Version
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