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This paper formulates and studies the problem of distributed filtering based on randomized gossip strategy in order to estimate the state of a dynamic system via all sensors in a network. First we introduce the randomized gossip algorithm by which the fastest averaging strategy can be obtained for a network with an arbitrary topology. Then we combine the randomized gossip algorithm with the information...
This paper studies and formulates the problem of distributed filtering with a diffusion strategy for state estimation of a dynamic system by using observations from sensors in a network. The sensor-nodes have estimation ability and work in a collaborative manner. The information transmission across the network abides by the diffusion strategy that each node communicates only with its neighbors. First,...
Different estimators have different optimization criteria according to the concrete application considered. Most existing metrics on estimation performance are some averages of estimation errors, which usually give “big” or “small” results to show the “bad” or “good” performance of the evaluated estimators. However, these metrics are only appropriate for measuring minimum mean-square error (MMSE),...
This paper studies the problem of simultaneous deciding on hypotheses and estimating a random parameter. We propose a joint decision and estimation (JDE) formulation, which amounts to minimizing a risk related to both decision and estimation while decision performance is also constrained within a tolerable level. The risk used in this paper is a weighted sum of estimation costs conditioned on correct...
How to fuse/combine state estimates that are obtained based on different models (e.g., a CV model, a CA model, and a CT model)? This paper provides a theoretical solution to such problems and beyond. Conventional multiple-model estimation methods use models defined in a common state space. In this paper, we discuss the advantage of using heterogeneous state space for different models in the multiple-model...
This paper deals with distributed estimation fusion under unknown cross-covariance between errors of local estimates. We propose a constraint to restrict the set of possible cross-covariance matrices first. Then this constraint, named allowance degree of cross-covariance, is used to derive a fusion method. Based on the allowance degree, we present an optimal robust fusion method in the minimax sense...
This paper deals with target classification by using both feature data and kinematic measurements. The problem is tackled by multi-hypothesis sequential testing with embedded target tracking. We implement Armitage's sequential probability ratio tests (SPRT) for non-maneuvering and maneuvering targets. Two track fusion architectures, including centralized fusion and distributed fusion, are used to...
This paper deals with distributed fusion with local quasi-tracklets and provides the optimal linear minimum mean-squared error (LMMSE) fusion, namely optimal quasi-tracklet fusion. We analyze its performance, present a necessary and sufficient condition under which the fusion is identical with the centralized fusion, and exploit its relationships with some existing distributed fusion methods. Numerical...
This paper deals with estimation fusion for a Markovian jump-linear system (MJLS) and proposes a distributed fusion scheme, in which local sensors send their transformed measurements to the fusion center and the fusion center fuses them with a multiple-model (MM) filter. A specific linear transformation for local measurements is studied and it is shown that the distributed minimum mean-squared error...
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