Functional Bayesian Filter

• Published in: IEEE transactions on signal processing Vol. 70; pp. 57 - 71
• Main Authors: Li, Kan, Principe, Jose C.
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive filters; Algorithms; Bayes methods; Bayesian analysis; Bayesian inference; Dynamical systems; Filtering; Filtering theory; Finite impulse response filters; Hilbert space; IIR filters; Impulse response; information theoretic learning; Inverse kinematics; Kalman filter; Kalman filters; Kernel; kernel adaptive filter; Kernel functions; kernel method; nonlinear estimation; reproducing kernel Hilbert space (RKHS); State estimation; State space models; System dynamics
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2021.3132277

We present a general nonlinear Bayesian filter for high-dimensional state estimation using the theory of reproducing kernel Hilbert space (RKHS). By applying the kernel method and the representer theorem to perform linear quadratic estimation in a functional space, we derive a Bayesian recursive state estimator for a general nonlinear dynamical system in the original input space. Unlike existing nonlinear extensions of the Kalman filter where the system dynamics are assumed known, the state-space representation for the Functional Bayesian Filter (FBF) is completely learned online from measurement data in the form of an infinite impulse response (IIR) filter or recurrent network in the RKHS, with universal approximation property. Using a positive definite kernel function satisfying Mercer's conditions to compute and evolve information quantities, the FBF exploits both the statistical and time-domain information about the signal, extracts higher-order moments, and preserves the properties of covariances without the ill effects due to conventional arithmetic operations. We apply this novel kernel adaptive filtering (KAF) to recurrent network training, chaotic time-series estimation and cooperative filtering using Gaussian and non-Gaussian noises, and inverse kinematics modeling. Simulation results show FBF outperforms existing Kalman-based algorithms.