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This document proposes an iterative nonlinear filtering algorithm based on variational Bayesian optimization. It approximates the posterior distribution of the system state using a variational distribution that is iteratively optimized to minimize the Kullback-Leibler divergence from the true posterior distribution. This converts the intractable integration in Bayesian filtering into an optimization problem. The performance of the proposed algorithm is evaluated on target tracking problems and compared to t

Author

anshul

Language

EN