@inproceedings{858285eefab04e11a2988b9c0ec8844b,
title = "Generation and estimation of randomly maneuvering target trajectories towards metacognitive tracking radar applications",
abstract = "There is growing research interest to merge the idea of a metacognitive radar with that of a tracking radar. The concept of metacognition can be broadly summarized as the process of learning about learning. In a metacognitive tracking radar, the system uses cognitive processes to detect and track a target in a dynamic environment. The radar then applies metacognitive techniques to select the cognitive process that yields the most accurate target track. In the context of target tracking, cognitive processes are various tracking algorithms. Currently, metacognitive tracking radar systems have only been demonstrated on targets of known trajectories. Their performance in the case of a randomly maneuvering target has not been explored. This paper presents an initial approach to this problem. First, an algorithm to generate random target trajectories is presented. Then, these trajectories are estimated using two estimation algorithms: the Extended Kalman Filter (EKF) and the Interacting Multiple Model (IMM) estimator. Finally, the performances of these two algorithms are compared.",
author = "Veena Sreekantamurthy and Devault, {Jordan A.} and Martone, {Anthony F.} and Kirk, {Benjamin H.} and Narayanan, {Ram M.}",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Radar Sensor Technology XXVI 2022 ; Conference date: 06-06-2022 Through 12-06-2022",
year = "2022",
doi = "10.1117/12.2622407",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Ranney, {Kenneth I.} and Raynal, {Ann M.}",
booktitle = "Radar Sensor Technology XXVI",
address = "United States",
}