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We propose a diffusion expectation-maximization algorithm with adaptive combiner for distributed estimation over sensor networks. Due to the spatial distribution of the nodes, variation of node profile across the network is a common phenomena in real applications. The unreliable nodes exist and provide inaccurate estimates, which may be caused by high levels of noise or malicious attacks. Instead...
The Sequential Probability Ratio Test (SPRT) is a classical detector for problems with an unfixed sample size. Though it is optimal under some conditions, SPRT can be directly used only for a binary hypothesis with exactly known distributions. In this paper, sequential detection problem with an uncertain hypothesis distribution is considered, in which the uncertain distribution is formulated in a...
Detection with multiple distributions is considered. Rather than formulating the problem with multiple hypotheses, we formulate the problem in a binary hypothesis testing framework by a multiple model approach. Three classes of the Multi-Model Detection (MMD) problems are considered: simplex, compound, and mixture. Three concepts of optimality are given for these three problems, including Uniformly...
A fault detection, identification, estimation and state estimation (FDIESE) problem involves joint decision and estimation (JDE). Decision contains detection and identification, while estimation is for fault severeness and system state. Both detection and identification are highly coupled with estimation and a fault is identified after detection. To solve this problem, an approach named nested joint...
The random matrix approach to extended objects tracking provides efficient estimation of both the states and the extensions. Then the Gaussian Inverse Wishart-Probability Hypothesis Density (GIW-PHD) filter in the random matrix framework is utilized to track multiple extended objects in the presence of clutter measurements and missed detections. In view of the invariant extension evolution model and...
Under the common state space model for tracking a maneuvering target, the tracker needs to adapt its state transition model timely to match the target maneuver, which is usually carried out by finding the best one from a bank of candidate Markov models or employing all of them simultaneously but assigning different probabilities. Both methods suffer from time delay for confirming the target maneuver...
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