@inproceedings{6e70db2e3aad4342a2efbfbd83da2e24,
title = "Generalized multiple-model adaptive estimation using an autocorrelation approach",
abstract = "In this paper a generalized multiple-model adaptive estimator is presented that can be used to estimate the unknown noise statistics in filter designs. The assumed unknowns in the adaptive estimator are the process noise covariance elements. Parameter elements generated from a quasi-random sequence are used to drive multiple-model parallel filters for state estimation. The current approach focuses on estimating the process noise covariance by sequentially updating weights associated with the quasi-random elements through the calculation of the likelihood function of the measurement-minus-estimate residuals, which also incorporates correlations between various measurement times. For linear Gaussian measurement processes the likelihood function is easily determined. For nonlinear Gaussian measurement processes, it is assumed, that the linearized output sufficiently captures the statistics of the likelihood function by making the small noise assumption. Simulation results, involving a two-dimensional target tracking problem using an extended Kalman filter, indicate that the new approach is able to correctly estimate the noise statistics.",
keywords = "Extended kalman filter, Filtering, Multiple-model adaptive estimation, Target tracking",
author = "Crassidis, \{John L.\} and Yang Cheng",
year = "2006",
doi = "10.1109/ICIF.2006.301651",
language = "English",
isbn = "1424409535",
series = "2006 9th International Conference on Information Fusion, FUSION",
publisher = "IEEE Computer Society",
booktitle = "2006 9th International Conference on Information Fusion, FUSION",
address = "United States",
note = "9th International Conference on Information Fusion, FUSION 2006 ; Conference date: 10-07-2006 Through 13-07-2006",
}