Trajectory Optimization of Autonomous Surface Vehicles with Outliers for Underwater Target Localization
Location awareness is crucial for underwater applications; without it, gathered data would be essentially useless. However, it is impossible to directly determine the location of an underwater target because GPS-reliant methods cannot be utilized in the underwater environment. To this end, the under...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/17/4343 |
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| author | Xiaojun Mei Dezhi Han Nasir Saeed Huafeng Wu Chin-Chen Chang Bin Han Teng Ma Jiangfeng Xian |
| author_facet | Xiaojun Mei Dezhi Han Nasir Saeed Huafeng Wu Chin-Chen Chang Bin Han Teng Ma Jiangfeng Xian |
| author_sort | Xiaojun Mei |
| collection | DOAJ |
| description | Location awareness is crucial for underwater applications; without it, gathered data would be essentially useless. However, it is impossible to directly determine the location of an underwater target because GPS-reliant methods cannot be utilized in the underwater environment. To this end, the underwater target localization technique has become one of the most critical technologies in underwater applications, wherein GPS-equipped autonomous surface vehicles (ASVs) are typically used to assist with localization. It has been proved that, under the assumption of Gaussian noise, an appropriate geometry among ASVs and the underwater target can enhance localization accuracy. Unfortunately, the conclusion may not hold if outliers arise and the closed-form expression of Cramér–Rao lower bound (CRLB) cannot be established. Eventually, it becomes hard to derive the accepted geometry, particularly for the received signal strength (RSS)-based ranging scenario. Therefore, this work optimizes the trajectory of ASVs with RSS-based ranging and in the presence of outliers to optimally estimate the location of an underwater target. The D-optimality criterion is applied in conjunction with the Monte Carlo method to determine the closed-form expression of the function, which then transforms the problem into an optimized framework. Nevertheless, the framework cannot be solved in the absence of the target location. In this case, the paper presents two methodologies to overcome the issue and achieve the optimum configuration without identifying the target location. (1) A min–max strategy that assumes that the target location drops in an uncertain region for a single or two ASVs is proposed; and (2) a two-phase localization approach (TPLA) that calculates the target location at each time slot for three ASVs is developed. Finally, the optimal trajectories of ASVs are constructed by a series of waypoints based on an analytically tractable measurement model in various conditions. |
| first_indexed | 2024-03-10T01:17:55Z |
| format | Article |
| id | doaj.art-42f174dab8f84ccf95e42e2fa8a5a8af |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T01:17:55Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-42f174dab8f84ccf95e42e2fa8a5a8af2023-11-23T14:05:03ZengMDPI AGRemote Sensing2072-42922022-09-011417434310.3390/rs14174343Trajectory Optimization of Autonomous Surface Vehicles with Outliers for Underwater Target LocalizationXiaojun Mei0Dezhi Han1Nasir Saeed2Huafeng Wu3Chin-Chen Chang4Bin Han5Teng Ma6Jiangfeng Xian7College of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaDepartment of Electrical and Communication Engineering, United Arab Emirates University, Al Ain 15551, United Arab EmiratesMerchant Marine College, Shanghai Maritime University, Shanghai 201306, ChinaDepartment of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, TaiwanShanghai Ship and Shipping Research Institute, Shanghai 200135, ChinaScience and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, ChinaInstitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaLocation awareness is crucial for underwater applications; without it, gathered data would be essentially useless. However, it is impossible to directly determine the location of an underwater target because GPS-reliant methods cannot be utilized in the underwater environment. To this end, the underwater target localization technique has become one of the most critical technologies in underwater applications, wherein GPS-equipped autonomous surface vehicles (ASVs) are typically used to assist with localization. It has been proved that, under the assumption of Gaussian noise, an appropriate geometry among ASVs and the underwater target can enhance localization accuracy. Unfortunately, the conclusion may not hold if outliers arise and the closed-form expression of Cramér–Rao lower bound (CRLB) cannot be established. Eventually, it becomes hard to derive the accepted geometry, particularly for the received signal strength (RSS)-based ranging scenario. Therefore, this work optimizes the trajectory of ASVs with RSS-based ranging and in the presence of outliers to optimally estimate the location of an underwater target. The D-optimality criterion is applied in conjunction with the Monte Carlo method to determine the closed-form expression of the function, which then transforms the problem into an optimized framework. Nevertheless, the framework cannot be solved in the absence of the target location. In this case, the paper presents two methodologies to overcome the issue and achieve the optimum configuration without identifying the target location. (1) A min–max strategy that assumes that the target location drops in an uncertain region for a single or two ASVs is proposed; and (2) a two-phase localization approach (TPLA) that calculates the target location at each time slot for three ASVs is developed. Finally, the optimal trajectories of ASVs are constructed by a series of waypoints based on an analytically tractable measurement model in various conditions.https://www.mdpi.com/2072-4292/14/17/4343received signal strength (RSS)autonomous surface vehicle (ASV)Fisher information matrixoptimal trajectoryD-optimality criterionoutliers |
| spellingShingle | Xiaojun Mei Dezhi Han Nasir Saeed Huafeng Wu Chin-Chen Chang Bin Han Teng Ma Jiangfeng Xian Trajectory Optimization of Autonomous Surface Vehicles with Outliers for Underwater Target Localization Remote Sensing received signal strength (RSS) autonomous surface vehicle (ASV) Fisher information matrix optimal trajectory D-optimality criterion outliers |
| title | Trajectory Optimization of Autonomous Surface Vehicles with Outliers for Underwater Target Localization |
| title_full | Trajectory Optimization of Autonomous Surface Vehicles with Outliers for Underwater Target Localization |
| title_fullStr | Trajectory Optimization of Autonomous Surface Vehicles with Outliers for Underwater Target Localization |
| title_full_unstemmed | Trajectory Optimization of Autonomous Surface Vehicles with Outliers for Underwater Target Localization |
| title_short | Trajectory Optimization of Autonomous Surface Vehicles with Outliers for Underwater Target Localization |
| title_sort | trajectory optimization of autonomous surface vehicles with outliers for underwater target localization |
| topic | received signal strength (RSS) autonomous surface vehicle (ASV) Fisher information matrix optimal trajectory D-optimality criterion outliers |
| url | https://www.mdpi.com/2072-4292/14/17/4343 |
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