Multi-targets tracking using parallel Kalman filter

The multi target tracking problem is considered in this paper and a parallel kalman filter algorithm (Decentralized Kalman Filter) is presented. The proposed algorithm designed to make early and more accurate estimate for dynamic multi-targets in the area and to determine whether the sensor data representing the same targets or not, when the ability to track targets is essential in missile defense. Proposed technique is a two stage data processing technique which processes data from multi sensor system. In the first stage, each local processor uses its own data to make a best local estimate using standerd kalman filter and then these estimates are then obtained in parallel processing mode to make best global estimate. The proposed model is tested when two sensors are used to track three targets and any sensor tracked two targets. The proposed technique performance is evaluated using measures such as the error covariance matrix and it gived high accuricy and optimal estimate. The experimental results showed that the proposed method has the ability to determine whether these two targets detected by the first sensor are the same two targets detected by the second sensor or two new targets.