Distributed data fusion algorithm for Wireless Sensor Network

Signal processing in Wireless Sensor Network (WSN) has a huge range of applications. Distributed Kalman Filter (DKF) is one of the most fundamental distributed estimation algorithms for scalable wireless sensor fusion. DKF finds applications in object tracking, environmental monitoring, surveillance, and many other applications. All algorithms proposed in the literature are based on static network. In reality, the network topology is changing. The topology change is often caused by node failure, which is due to energy depletion. In this work a DKF is proposed for such network. The simulation and the experimental results validate our proposed DKF. The experimental results show that each sensor node can run DKF with up to six neighbors.