Analysis of Kalman Filter Approximations for Nonlinear Measurements

作者：Morelande Mark R^*; Garcia Fernandez Angel F

来源：IEEE Transactions on Signal Processing, 2013, 61(22): 5477-5484.

DOI：10.1109/TSP.2013.2279367

• Bayesian filtering
• Kalman filtering
• nonlinear measurement
• Kullback-Leibler divergence

A theoretical analysis is presented of the correction step of the Kalman filter (KF) and its various approximations for the case of a nonlinear measurement equation with additive Gaussian noise. The KF is based on a Gaussian approximation to the joint density of the state and the measurement. The analysis metric is the Kullback-Leibler divergence of this approximation from the true joint density. The purpose of the analysis is to provide a quantitative tool for understanding and assessing the performance of the KF and its variants in nonlinear scenarios. This is illustrated using a numerical example.

• 出版日期2013-11