A decomposition method in the multivariate feedback particle filter via tensor product Hermite polynomials

Abstract

The feedback particle filter (FPF), a resampling-free algorithm proposed over a decade ago, modifies the particle filter (PF) by incorporating a feedback structure. Each particle in FPF is regulated via a feedback gain function (lacking a closed-form expression), which solves a Poisson's equation with a probability-weighted Laplacian. While approximate solutions to this equation have been extensively studied in recent literature, no efficient multivariate algorithm exists. In this paper, we focus on the decomposition method for multivariate gain functions in FPF, which has been proven efficient for scalar FPF with polynomial observation functions. Its core is splitting the Poisson's equation into two exactly solvable sub-equations. Key challenges in extending it to multivariate FPF include ensuring the invertibility of the coefficient matrix in one sub-equation and constructing a weighted-radial solution in the other. The proposed method's computational complexity grows at most polynomially with the state dimension, a dramatic improvement over the exponential growth of most particle-based algorithms. Numerical experiments compare the decomposition method with traditional methods: the extended Kalman filter (EKF), PF, and FPF with constant-gain or kernel-based gain approximations. Results show it outperforms PF and FPF with other gain approximations in both accuracy and efficiency, achieving the shortest CPU time among methods with comparable performance.