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

摘要: Signal processing is a ubiquitous part of modern technology. Target tracking one the very important applications Wireless Sensor Networks (WSN) and wireless sensor nodes provide accurate information since they can be deployed operated near phenomenon. These sensing have possibility collaboration among themselves to progress target detection precisions. Traditionally, Kalman filter its derivatives [1–3] are some most popular algorithms in solving signal problem. This paper focuses about state observed polar coordinates i.e. if collect range bearing data then easily converted Cartesian using inverse transformation filter. Discrete Time-Varying applied for test signals denoised tackle problems. Finally, Performance experimental results measured Measured Error Covariance Estimation Covariance.