An Adaptive Consensus Filter for Distributed State Estimation With Unknown Noise Statistics
Xiangxiang Dong, Giorgio Battistelli, Luigi Chisci, Yunze Cai
Abstract
An adaptive consensus filter for sensor networks with unknown process and measurement noise statistics is proposed in this letter. The variational Bayes(VB) approach is exploited to get local estimates of unknown noise covariances with prior inverse Wishart distributions. A distributed averaging approach on exponential-class densities is applied for consensus on the natural parameters of the unknown predicted error covariance. Consensus on measurements is performed in parallel and the two consensus outcomes are fused. Simulation results demonstrate the effectiveness of the proposed adaptive consensus filter compared to conventional, non-adaptive, consensus filters.
Topics & Concepts
Adaptive filterFilter (signal processing)Noise (video)Noise measurementCovarianceComputer scienceConsensusBayes' theoremMathematicsExponential familyAlgorithmStatisticsControl theory (sociology)Artificial intelligenceBayesian probabilityMulti-agent systemNoise reductionComputer visionControl (management)Image (mathematics)Target Tracking and Data Fusion in Sensor NetworksDistributed Sensor Networks and Detection AlgorithmsDistributed Control Multi-Agent Systems