Mitigation of Target Tracking Errors and sUAS Response Using Multi Sensor Data Fusion

Developing tactical or strategic methods to counter the small Unmanned Aerial System (sUAS) threats is effectively pacing up. With the advent of unprecedented proliferation of malicious or unintended intrusion from drones, the national infrastructure could be at risk and can become vulnerable if detection, tracking and disruption of these sUAS employed with electronic counter measures are at stake. Anticipating the boom in counter UAS technology, this paper presents methods and state estimation techniques based on multi sensor data fusion to mitigate position errors caused by electronic counter measures. A complete mathematical modeling and simulation of the proposed system for further research is presented. Two sensors namely RADAR and FLIR (Forward Looking Infrared) and their mathematical models are considered in this paper. A state variable approach to describe the motion characteristics of the target and sensor measurement model is utilized and performance evaluation of tracking filters are investigated. The experimental results in MATLAB show fusion architectures that demonstrate better tracking results with less residual errors. Also, for a nonlinear target motion the robust particle filter proves its nature and achieves desired response.