Improving tracking accuracy using information of dissimilar sensors

Making use of information acquired from a sensor network to improve the accuracy of target tracking is one of the most important issues in sensor network research. This paper demonstrates this philosophy using a distributed dissimilar sensor fusion scenario, where a tracker was established and maintained by a surface radar sensor and the distributed sensor fusion is performed whenever target measurement from an angle-only sensor is available to the radar sensor. The target state information is extracted from the angle-only sensor measurement so that the distributed track fusion at radar sensor can be performed. The extended Kalman filters (EKF) have been used to implement all tracking functions due to the nonlinearity between target state and the associated sensor observations. The scenario is conveniently implemented using advanced radar tracking system (ARTS) toolbox in Matlab Simulink environment. Our simulation results have shown the improvement of the (racking accuracy bv applying distributed track fusion. The convenience of using ARTS toolbox for complex algorithm implementation and testing are also clear from the context.