Denoising the signals using Kalman filter for target tracking in wireless sensor networks

Signal processing is a ubiquitous part of modern technology. Target tracking is one of the very important applications of Wireless Sensor Networks (WSN) and wireless sensor nodes provide accurate information since they can be deployed and operated near the phenomenon. These sensing nodes have the possibility of collaboration among themselves to progress the target detection and tracking precisions. Traditionally, Kalman filter and its derivatives are some of the most popular algorithms in solving the signal tracking problem. This paper focuses about the target state observed in polar coordinates i.e. if the nodes collect range and bearing data then they can be easily converted to Cartesian coordinates using inverse transformation and Kalman filter. Discrete Kalman filter and Time-Varying Kalman filter are applied for test signals and denoised to tackle target tracking problems. Finally, Performance of the experimental results are measured using Measured Error Covariance and Estimation Error Covariance.