Data Sensor Fusion for Surveillance Applications: Evaluation of Extended Kalman Filter vs. Unscented Kalman Filter

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Abstract
This chapter introduces the foundations of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) for data sensor fusion applications in surveillance applications. After explaining how to model the drone under the constant turn rate and velocity (CTRV) dynamics, the EKF and UKF techniques for Lidar and Radar data sensor fusion are applied to enable better 3D object detection and reconstruction from point cloud data is evaluated under a performance comparison. Through root mean square error (RMSE) and mean square error (MSE) the filters are designed to meet the specifications of the model based on the position of the gathered points (X and Y axis) in the space state model. By degrading the measurements with Gaussian and Rayleigh noise, the results evaluate and compare both techniques to show the tolerance and performance in terms of stability, scalability, and application to drone monitoring in surveillance activities.

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