A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets
In this paper, a Monte Carlo (MC)-based extended Kalman filter is proposed for a two-dimensional bearings-only tracking problem (BOT). This problem addresses the processing of noise-corrupted bearing measurements from a sea acoustic source and estimates state vectors including position and velocity....
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/7/2087 |
| _version_ | 1797212038285492224 |
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| author | Sahab Edrisi Javad Enayati Abolfazl Rahimnejad Stephen Andrew Gadsden |
| author_facet | Sahab Edrisi Javad Enayati Abolfazl Rahimnejad Stephen Andrew Gadsden |
| author_sort | Sahab Edrisi |
| collection | DOAJ |
| description | In this paper, a Monte Carlo (MC)-based extended Kalman filter is proposed for a two-dimensional bearings-only tracking problem (BOT). This problem addresses the processing of noise-corrupted bearing measurements from a sea acoustic source and estimates state vectors including position and velocity. Due to the nonlinearity and complex observability properties in the BOT problem, a wide area of research has been focused on improving its state estimation accuracy. The objective of this research is to present an accurate approach to estimate the relative position and velocity of the source with respect to the maneuvering observer. This approach is implemented using the iterated extended Kalman filter (IEKF) in an MC-based iterative structure (MC-IEKF). Re-linearizing dynamic and measurement equations using the IEKF along with the MC campaign applied to the initial conditions result in significantly improved accuracy in the estimation process. Furthermore, an observability analysis is conducted to show the effectiveness of the designed maneuver of the observer. A comparison with the widely used UKF algorithm is carried out to demonstrate the performance of the proposed method. |
| first_indexed | 2024-04-24T10:36:02Z |
| format | Article |
| id | doaj.art-bbcf3d332e1c4faca1a43a3aa07d6443 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T10:36:02Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-bbcf3d332e1c4faca1a43a3aa07d64432024-04-12T13:26:10ZengMDPI AGSensors1424-82202024-03-01247208710.3390/s24072087A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea TargetsSahab Edrisi0Javad Enayati1Abolfazl Rahimnejad2Stephen Andrew Gadsden3Parent Company of Iran Telecommunications Infrastructure, Babol 4714745387, IranSander Elektronik, Stauseestrasse 73, CH-5314 Böttstein, SwitzerlandFaculty of Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaFaculty of Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaIn this paper, a Monte Carlo (MC)-based extended Kalman filter is proposed for a two-dimensional bearings-only tracking problem (BOT). This problem addresses the processing of noise-corrupted bearing measurements from a sea acoustic source and estimates state vectors including position and velocity. Due to the nonlinearity and complex observability properties in the BOT problem, a wide area of research has been focused on improving its state estimation accuracy. The objective of this research is to present an accurate approach to estimate the relative position and velocity of the source with respect to the maneuvering observer. This approach is implemented using the iterated extended Kalman filter (IEKF) in an MC-based iterative structure (MC-IEKF). Re-linearizing dynamic and measurement equations using the IEKF along with the MC campaign applied to the initial conditions result in significantly improved accuracy in the estimation process. Furthermore, an observability analysis is conducted to show the effectiveness of the designed maneuver of the observer. A comparison with the widely used UKF algorithm is carried out to demonstrate the performance of the proposed method.https://www.mdpi.com/1424-8220/24/7/2087iterative extended Kalman filter (IEKF)Monte Carlo (MC) simulationposition and velocity estimationbearings-only tracking (BOT) |
| spellingShingle | Sahab Edrisi Javad Enayati Abolfazl Rahimnejad Stephen Andrew Gadsden A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets Sensors iterative extended Kalman filter (IEKF) Monte Carlo (MC) simulation position and velocity estimation bearings-only tracking (BOT) |
| title | A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets |
| title_full | A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets |
| title_fullStr | A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets |
| title_full_unstemmed | A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets |
| title_short | A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets |
| title_sort | monte carlo based iterative extended kalman filter for bearings only tracking of sea targets |
| topic | iterative extended Kalman filter (IEKF) Monte Carlo (MC) simulation position and velocity estimation bearings-only tracking (BOT) |
| url | https://www.mdpi.com/1424-8220/24/7/2087 |
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