Intensive Review of Drones Detection and Tracking: Linear Kalman Filter Versus Nonlinear Regression, an Analysis Case
Archives of Computational Methods in Engineering
In this paper, an extensive review for objects and drones (AUVs) detection and tracking is presented. The article presents state of the art methods used in detection and tracking of drones with adequate analysis and comparisons summarizing the findings of the most recent research material in that field. The most famous technique used in drones tracking is Kalman Filters (KFs) in its different forms. The paper presents analysis and comparisons for drones tracking based on Linear Kalman Filters (LKF) compared to tracking using Nonlinear Polynomial Regression (NPR) techniques. Interesting findings reflect the need for both methods at different circumstances depending on the noise conditions of the measurements. On the other hand, many new methods such as Artificial Intelligence (AI) based techniques are recently used in drones detection and recognition. Detection methods could come separate or combined with tracking techniques. The work presents broad and deep literature review with critical analysis of most famous methods used in drones detection and tracking.