Piecewise Optimal Trajectories of Observer for Bearings-Only Tracking of Maneuvering Target

Autor: François Dufour, Huilong Zhang, Adrien Negre, Dann Laneuville, Jonatha Anselmi
Přispěvatelé: Quality control and dynamic reliability (CQFD), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut Polytechnique de Bordeaux (Bordeaux INP), Naval Group, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Proceedings of IEEE Aerospace Conference
Proceedings of IEEE Aerospace Conference, Mar 2018, Bigsky, United States
Popis: This paper provides a general framework for optimizing the trajectory of an observer by means of bearings-only tracking (BOT) in the case where the source is maneuvering. The basic problem of target motion analysis (TMA) is to estimate the trajectory of an object from noise corrupted sensor data. In the BOT context, the source state is only partially observed through nonlinear measurements, and the quality of the estimation strongly depends on the observer trajectory. We assume that the source has maneuvering capabilities, which is a case of practical interest though the TMA performance may be dramatically degraded. To characterize more precisely observer-optimal maneuvers, we have to be able to determine the optimal course. To this purpose, the common approach consists in maximizing the determinant of the Fisher information matrix (FIM) by an iterative algorithm to obtain an optimal observer trajectory. In our framework, a cumulative sum of bearing rates and relative distance between the target and observer is used as reward function, and the chosen maneuver will be the one that maximizes the mathematical expectation of the reward function. The target's motion is quantized, and an extended Kalman filter is used to estimate the position and velocity of the target and to provide the initial distribution for each optimization period. The proposed algorithm is a closed-loop control, and by maximizing the reward function, it allows to automatically compute the suboptimal maneuver. The applicability of our approach is demonstrated on realistic scenarios.
Databáze: OpenAIRE