Resolution of Left-Right Ambiguity for Underwater Bearing-Only Tracking

During bearing-only tracking (BOT) with a towed array sensor, the array can sense the bearing angle of the target but is unable to differentiate whether the target is on the left or the right side of the array. Thus, the traditional tracking algorithm generates tracks on both sides of the array, which creates difficulties when interception is required. In this article, we propose a method based on the likelihood of measurement, which, along with the estimators, can resolve left-right ambiguity (L-R ambiguity) and track the target. The proposed method is implemented for a target following a constant velocity model and a coordinated turn (CT) model. The tracking results are compared in terms of the root-mean-square error (RMSE) in position and velocity, bias norm, percentage of track loss, the relative execution time, and flop counts. We also provide the convergence analysis of the proposed method, which indicates that it converges on the target’s trajectory, effectively resolving the L-R ambiguity. We additionally examine the robustness of the proposed method with respect to initial uncertainty in terms of the average RMSE in position and velocity, as well as the track loss percentage.

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Publication Details

Journal
IEEE Sensors Journal
Published
2024-08-19
DOI
https://doi.org/10.1109/jsen.2024.3438997
Citations
2
Primary Topic
Target Tracking and Data Fusion in Sensor Networks
Type
article
Field-Weighted Citation Impact
0.69
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article

Resolution of Left-Right Ambiguity for Underwater Bearing-Only Tracking

Ranjeet Kumar Tiwari, Shovan Bhaumik, Shreya Das
2 citations
IEEE Sensors Journal
Target Tracking and Data Fusion in Sensor Networks
0.69
article

Resolution of Left-Right Ambiguity for Underwater Bearing-Only Tracking

Ranjeet Kumar Tiwari, Shovan Bhaumik, Shreya Das
article en
2 citations

Abstract

During bearing-only tracking (BOT) with a towed array sensor, the array can sense the bearing angle of the target but is unable to differentiate whether the target is on the left or the right side of the array. Thus, the traditional tracking algorithm generates tracks on both sides of the array, which creates difficulties when interception is required. In this article, we propose a method based on the likelihood of measurement, which, along with the estimators, can resolve left-right ambiguity (L-R ambiguity) and track the target. The proposed method is implemented for a target following a constant velocity model and a coordinated turn (CT) model. The tracking results are compared in terms of the root-mean-square error (RMSE) in position and velocity, bias norm, percentage of track loss, the relative execution time, and flop counts. We also provide the convergence analysis of the proposed method, which indicates that it converges on the target’s trajectory, effectively resolving the L-R ambiguity. We additionally examine the robustness of the proposed method with respect to initial uncertainty in terms of the average RMSE in position and velocity, as well as the track loss percentage.

IEEE Sensors JournalVol. 24(19)
Indian Institute of Technology Patna (IN), The University of Adelaide (AU)
Openalex Percentile: Top 25%
Target Tracking and Data Fusion in Sensor Networks
0.69
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