Fully Decentralized Weighted Kalman Filter for Wireless Sensor Networks with FuzzyART Neural Networks
A framework for developing a decentralized weighted Kalman filter with FuzzyART neural networks is presented. Active sensors communication is restricted to a node-to-node basis in the vicinity of each cluster where readings are only shared between neighbors. FuzzyART neural network takes these readings and classifies them into categories based on estimated measurement accuracy where appropriate weights are assigned to each measurement according to a decision tree. Few modifications have been made to the traditional Information Kalman Filter to adapt with the algorithm developed herein. The FuzzyART model is layered below the Kalman filter in a way it detects faulty measurements and spatial alteration of the target and accommodates these changes to better estimate the target. Sensors with faulty measurements are inhibited form participating in the estimation process and its readings are neither processed nor further communicated thus achieving higher energy efficiency. Furthermore, introducing a FuzzyART model lies within the complexity constraints of a sensor node and have acceptable overhead. Results and simulations show the superiority of the proposed algorithm when compared with the traditional Information Filter.