An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model

Thelong-distance coverage of high-frequency surface wave radar (HFSWR) has promoted it as an enormous means for ship monitoring on the country’s maritime territory. Since it is a primaryradar, noncooperative targets can also be detected. However, this radar also has a shortcoming of low spatial and...

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Main Authors: null Iswandi, Risanuri Hidayat, Sigit B. Wibowo
Format: Article
Language:English
Published: Wiley 2023-01-01
Series:Journal of Engineering
Online Access:http://dx.doi.org/10.1155/2023/1481943
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author null Iswandi
Risanuri Hidayat
Sigit B. Wibowo
author_facet null Iswandi
Risanuri Hidayat
Sigit B. Wibowo
author_sort null Iswandi
collection DOAJ
description Thelong-distance coverage of high-frequency surface wave radar (HFSWR) has promoted it as an enormous means for ship monitoring on the country’s maritime territory. Since it is a primaryradar, noncooperative targets can also be detected. However, this radar also has a shortcoming of low spatial and temporal resolutions due to the narrow available bandwidth in the HF band. This limitation can reduce the performance of ship detection and tracking, especially for highly maneuvering ships. This paper proposes a new method to assess the tracking algorithm for a high-maneuvering ship. The absence of a high-maneuvering plot in the AIS data and existing analytical models are replaced by the MMG model run on MANSIM software. The linear, turning, and zigzag motions are generated and used to evaluate the tracking algorithms. The Monte Carlo simulation was conducted regarding the degradation of spatial resolution in the higher radial range. The tracking performance was analyzed by calculating the RMSE of four parameters, i.e., absolute position, radial range, bearing angle, and speed. For a trial case, four tracking algorithms were evaluated, i.e., Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). The evaluation results showed that the EKF tracker had a minor error for the linear track with RMSE of absolute position, radial range, bearing angle, and speed being 1.368 km, 0.526 km, 1.550°, and 0.005 m/s, respectively. Otherwise, the UKF performed slightly better than EKF for the high maneuver targets. The RMSE of absolute position, radial range, bearing angle, and speed were 1.649 km, 0.639 km, 1.919°, and 0.165 m/s, respectively. The results also ensure the applicability of the MMG model to evaluate the tracking algorithm’s performance in HFSWR.
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spelling doaj-art-6b625aa6f27f48dba7e968fdcc7178ac2025-08-20T02:23:59ZengWileyJournal of Engineering2314-49122023-01-01202310.1155/2023/1481943An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Modelnull Iswandi0Risanuri Hidayat1Sigit B. Wibowo2Department of Electrical Engineering and Information TechnologyDepartment of Electrical Engineering and Information TechnologyDepartment of Electrical Engineering and Information TechnologyThelong-distance coverage of high-frequency surface wave radar (HFSWR) has promoted it as an enormous means for ship monitoring on the country’s maritime territory. Since it is a primaryradar, noncooperative targets can also be detected. However, this radar also has a shortcoming of low spatial and temporal resolutions due to the narrow available bandwidth in the HF band. This limitation can reduce the performance of ship detection and tracking, especially for highly maneuvering ships. This paper proposes a new method to assess the tracking algorithm for a high-maneuvering ship. The absence of a high-maneuvering plot in the AIS data and existing analytical models are replaced by the MMG model run on MANSIM software. The linear, turning, and zigzag motions are generated and used to evaluate the tracking algorithms. The Monte Carlo simulation was conducted regarding the degradation of spatial resolution in the higher radial range. The tracking performance was analyzed by calculating the RMSE of four parameters, i.e., absolute position, radial range, bearing angle, and speed. For a trial case, four tracking algorithms were evaluated, i.e., Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). The evaluation results showed that the EKF tracker had a minor error for the linear track with RMSE of absolute position, radial range, bearing angle, and speed being 1.368 km, 0.526 km, 1.550°, and 0.005 m/s, respectively. Otherwise, the UKF performed slightly better than EKF for the high maneuver targets. The RMSE of absolute position, radial range, bearing angle, and speed were 1.649 km, 0.639 km, 1.919°, and 0.165 m/s, respectively. The results also ensure the applicability of the MMG model to evaluate the tracking algorithm’s performance in HFSWR.http://dx.doi.org/10.1155/2023/1481943
spellingShingle null Iswandi
Risanuri Hidayat
Sigit B. Wibowo
An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model
Journal of Engineering
title An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model
title_full An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model
title_fullStr An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model
title_full_unstemmed An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model
title_short An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model
title_sort evaluation method of ship tracking algorithms for high frequency surface wave radar considering high maneuvers generated by the mmg model
url http://dx.doi.org/10.1155/2023/1481943
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