A State Estimation and Fusion Algorithm for High-speed Low-altitude Targets

Autonomous systems require situational awareness that is resilient to unusual but realistic conditions. Modern maritime defense systems are equipped with highly intelligent tracking sensors to detect and track high-speed incoming threats. Such systems with single sensor are not reliable and accurate. The paper suggests multi-sensor data fusion approach to overcome the limitations of a single sensor. The sensors used in this study are Laser Detection And Ranging (LADAR) and infrared (IR). The information obtained from these sensors is fused to achieve 3-D localization of high-speed incoming threats. The Kalman and extended Kalman filter are employed for optimum state estimates and data fusion. Computer simulations clearly demonstrate the efficiency of the proposed algorithm. Performance of the presented fusion approach in comparison with other existing approaches is also presented. Computational time of each technique is also computed for comparison.