Variational Compensation Based Nonlinear Filter for Continuous-Discrete Stochastic Systems

EasyChair Preprint 3758

Abstract

In this paper, a novel variational compensation based nonlinear filter (VCNF) is proposed to copy with the nonlinear filtering problem in continuous-discrete systems. The core of VCNF is to construct a variational state compensation model with variational compensation parameters for accurately describing uncertain continuous state. The role of variational compensation parameters is to adaptively compensate the unpredictable approximation and discretization errors of system states. In the variational Bayesian framework, through iteratively and alternatively achieving the fitting of the state priori model and the compensation of approximation and discretization errors, estimation accuracy and adaptiveness can be enhanced gradually. The superior performance of VCNF is demonstrated in the simulation of target tracking.

Keyphrases: Continuous-discrete stochastic system, Nonlinear Kalman filter, Variational Bayesian method, target tracking

@booklet{EasyChair:3758,
  author    = {Tingjun Wang and Haoran Cui and Xiaoxu Wang},
  title     = {Variational Compensation Based Nonlinear Filter for Continuous-Discrete Stochastic Systems},
  howpublished = {EasyChair Preprint 3758},
  year      = {EasyChair, 2020}}