期刊
REMOTE SENSING
卷 13, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/rs13051014
关键词
sonar target tracking; AUV; anti-collision; Kalman filter; underwater surveillance
类别
资金
- Ministry of Science and Higher Education for statutory activities in the Maritime University of Szczecin [1/S/KG/21]
The study focused on defining process noise in underwater target tracking, using Gaussian noise and Kalman filtering algorithms to estimate the target tracking state vector, verifying and comparing existing methods, and ultimately drawing conclusions.
Target tracking is a process that provides information about targets in a specific area and is one of the key issues affecting the safety of any vehicle navigating in water. The main sensor used for underwater target tracking is sonar, with one of the most popular configurations being forward looking sonar (FLS). The target tracking state vector is usually estimated with the use of numerical filter algorithms, such as the Kalman filter (KF) and its modification, or the particle filter (PF). This requires the definition of a process model, including process noise, and a measurement model. This study focused on process noise definition. It is usually implemented as Gaussian noise, with a covariance matrix defined by the author. An analytical and empirical analysis was conducted, including a verification of the existing approaches and a survey of the published literature. Additionally, a theoretical analysis of the factors influencing process noise was conducted, which was followed by an empirical verification. The results were discussed, leading to the conclusions. The results of the theoretical analysis were confirmed by the empirical experiment and the results were compared with commonly used values of process noise in underwater target tracking processes.
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