Search

A new class of variable-structure (VS) algorithms for multiple-model (MM) estimation is presented, referred to as expected-mode augmentation (EMA). In the EMA approach, the original set of models is augmented by a variable set of models intended to match the expected value of the unknown true mode. These models are generated adaptively in real time as (globally or locally) probabilistically weighted sums of mode estimates over the model set This makes it possible to cover a large continuous mode space by a relatively small number of models at a given accuracy level. The paper presents new theoretical results for model-set design, a general formulation of the EMA approach, along with theoretical analysis and justification, and three algorithms for its practical implementation. The performance of the proposed EMA algorithms is evaluated via simulation of a generic maneuvering target tracking problem. [ABSTRACT FROM AUTHOR]

The performance of multiple-model filtering algorithms is examined for shock-variance models, which are a form of linear Gaussian switching models. The primary aim is to determine whether existing multiple-model filters are suitable for evaluating measurement likelihoods in classification applications, and under what conditions such classification models are viable. Simulation experiments are used to empirically examine the likelihood-evaluation performance of suboptimal merging and pruning algorithms as the number of state hypotheses per time step (i.e., algorithm order) increases. The second-order generalized pseudo-Bayes or (GPB(2)) algorithm is found to provide excellent performance relative to higher order GPB algorithms through order five. Likelihoods from fixed-size pruning (FSP) algorithms with increasing numbers of state hypotheses are used to validate the GPB likelihoods, and convergence of the FSP likelihoods to the GPB values is observed. These results suggest that GPB(2) is a reasonable approximation to the unrealizable optimal algorithm for classification. In all cases except very-low-noise situations, the interacting multiple model (1MM) algorithm is found to provide an adequate approximation to GPB(2). Sensitivity of likelihood estimates to certain model parameters is also investigated via a mismatch analysis. As a classification tool, the discrimination capabilities of the measurement likelihoods are tested using an idealized forced-choice experiment, both with ideal and with mismatched models. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel call admission control scheme named overlap area assisted new call blocking scheme (NCBS-OA) for High Altitude Platform (HAP) based communications systems, which utilizes both the geographical information and the overlap area to intelligently decide when to block a new call to help avoid possible handover failure. An approximate analytical model is derived to estimate the new call blocking performance of the new scheme, which is verified by simulation results. Research results show that the NCBS-OA can improve the new call blocking probability significantly (by a minimum of 25% for the system model with the parameter values chosen) while maintaining a zero handover call dropping probability seen with the time-based channel reservation algorithm (TCRA), and that the greater the cell radius and the more the number of connections considered in the overlap area of a virtual cell, the better the new call blocking performance. [ABSTRACT FROM AUTHOR]