Generalized Multiple-Model Adaptive Estimation Using an Autocorrelation Approach Badr N. Alsuwaidan National Satellite Technology Program, KACST, Riyadh, Saudi Arabia John L. Crassidis University at Buffalo, State University of New York, Amherst, NY 14260-4400 Yang Cheng Mississippi State University, Mississippi State, MS 39762 Abstract In this paper a generalized multiple-model adaptive estimator is presented that can be used to estimate unknown model and/or filter parameters, such as the noise statistics in filter designs. The main goal of this work is to provide an increased convergence rate for the estimated model parameters over the traditional multiple-model adaptive estimator. Parameter elements generated from a quasi-random sequence are used to drive multiple 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. A proof is provided that shows the convergence properties of the generalized approach versus the standard multiple-model adaptive estimator. Simulation results, involving a two-dimensional target tracking problem and a single-axis attitude problem, indicate that the new approach provides better convergence properties over a traditional multiple-model approach. Index Terms Generalized Multiple-Model Adaptive Estimation, Correlation, Residual. I. I NTRODUCTION Multiple-model adaptive estimation (MMAE) uses a parallel bank of filters to provide multiple estimates, where each filter corresponds with a dependence on some unknowns. The state estimate is provided through a sum of each filter’s estimate weighted by the likelihood of the unknown elements conditioned on the measurement sequence. The likelihood function gives the associated hypothesis that each filter is the correct one. Many applications of MMAE approaches exist [1]. Three generations of MMAE have been characterized. The first is the classical approach presented in Ref. 2. The second is the interacting multiple-model algorithm [3] and the third is variable structure multiple-model estimation [4]. Judiciously determining the filter bank size has also been an active area of research [5]. Adaptive filtering can be divided into four general categories: Bayesian, maximum likelihood, covariance matching, and correlation approaches [6]. Bayesian and maximum likelihood methods may be well suited to multiple-model approaches, but sometimes require large computational loads. Covariance matching Badr Alsuwaidan is a Research Assistant Professor at the Space Research Institute, KACST. Email: bswauidan@kacst.edu.sa John Crassidis is a Professor in the Department of Mechanical & Aerospace Engineering. Email: johnc@buffalo.edu Yang Cheng is an Assistant Professor in the Department of Aerospace Engineering. Email: cheng@ae.msstate.edu