BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors

Shalev, Hadar; Klein, Itzik

doi:10.3390/s21134457

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…In [ 19 ], a distributed array configuration control scheme was proposed based on bearing-only sensor detection. In [ 20 ], a deep learning framework for the passive sensor detection process was also proposed; however, this method relied on datasets and had limited real-time performance.…”

Section: Introductionmentioning

confidence: 99%

Layout of Detection Array Based on Multi-Strategy Fusion Improved Adaptive Mayfly Algorithm in Bearing-Only Sensor Network

Chen,

Fang,

Zhang

et al. 2024

Sensors

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The various applications of bearing-only sensor networks for detection and localization are becoming increasingly widespread and important. The array layout of the bearing-only sensor network seriously impacts the detection performance. This paper proposes a multi-strategy fusion improved adaptive mayfly algorithm (MIAMA) in a bearing-only sensor network to perform layout planning on the geometric configuration of the optimal detection. Firstly, the system model of a bearing-only sensor network was constructed, and the observability of the system was analyzed based on the Cramer–Rao Lower Bound and Fisher Information Matrix. Then, in view of the limitations of the traditional mayfly algorithm, which has a single initial population and no adaptability and poor global search capabilities, multi-strategy fusion improvements were carried out by introducing Tent chaos mapping, the adaptive inertia weight factor, and Random Opposition-based Learning. Finally, three simulation experiments were conducted. Through comparison with the Particle Swarm Optimization (PSO) algorithm, Mayfly Algorithm (MA), and Genetic Algorithm (GA), the effectiveness and superiority of the proposed MIAMA were validated.

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Section: Introductionmentioning

confidence: 99%

Layout of Detection Array Based on Multi-Strategy Fusion Improved Adaptive Mayfly Algorithm in Bearing-Only Sensor Network

Chen,

Fang,

Zhang

et al. 2024

Sensors

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“…A recently published work used a deep learning method to solve the bearing-only localization problem instead of using the model-based iterative least squares estimator Shalev and Klein (2021). They showed, by simulation, that a datadriven deep learning approach performs better than the iterative least squares.…”

Section: Introductionmentioning

confidence: 99%

“…They showed, by simulation, that a datadriven deep learning approach performs better than the iterative least squares. Although Shalev and Klein (2021) working on a nonlinear problem with different sensors and a localization problem instead of a navigation problem, it gives a good indication that deep learning can obtain better results compared to a standard parameter estimator such as LS.…”

Section: Introductionmentioning

confidence: 99%

BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation

Cohen,

Klein

2022

Preprint

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Autonomous underwater vehicles (AUV) perform various applications such as seafloor mapping and underwater structure health monitoring. Commonly, an inertial navigation system aided by a Doppler velocity log (DVL) is used to provide the vehicle's navigation solution. In such fusion, the DVL provides the velocity vector of the AUV, which determines the navigation solution's accuracy and helps estimate the navigation states. This paper proposes BeamsNet, an end-to-end deep learning framework to regress the estimated DVL velocity vector that improves the accuracy of the velocity vector estimate, and could replace the model-based approach. Two versions of BeamsNet, differing in their input to the network, are suggested. The first uses the current DVL beam measurements and inertial sensors data, while the other utilizes only DVL data, taking the current and past DVL measurements for the regression process. Both simulation and sea experiments were made to validate the proposed learning approach relative to the model-based approach. Sea experiments were made with the Snapir AUV in the Mediterranean Sea, collecting approximately four hours of DVL and inertial sensor data. Our results show that the proposed approach achieved an improvement of more than 60% in estimating the DVL velocity vector.

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“…Among other approaches, bearing-only target tracking [11][12][13] represents an increasingly popular topic, with application scenarios ranging from underwater tracking [14,15] to cooperative tracking for multiagent systems [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements

Oliva

Farina

Setola

2021

Sensors

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This paper develops a framework to track the trajectory of a target in 2D by considering a moving ownship able to measure bearing measurements. Notably, the framework allows one to incorporate additional information (e.g., obtained via intelligence) such as knowledge on the fact the target’s trajectory is contained in the intersection of some sets or the fact it lies outside the union of other sets. The approach is formally characterized by providing a constrained maximum likelihood estimation (MLE) formulation and by extending the definition of the Cramér–Rao lower bound (CRLB) matrix to the case of MLE problems with inequality constraints, relying on the concept of generalized Jacobian matrix. Moreover, based on the additional information, the ownship motion is chosen by mimicking the Artificial Potential Fields technique that is typically used by mobile robots to aim at a goal (in this case, the region where the target is assumed to be) while avoiding obstacles (i.e., the region that is assumed not to intersect the target’s trajectory). In order to show the effectiveness of the proposed approach, the paper is complemented by a simulation campaign where the MLE computations are carried out via an evolutionary ant colony optimization software, namely, mixed-integer distributed ant colony optimization solver (MIDACO-SOLVER). As a result, the proposed framework exhibits remarkably better performance, and in particular, we observe that the solution is less likely to remain stuck in unsatisfactory local minima during the MLE computation.

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BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors

Cited by 10 publications

References 24 publications

Layout of Detection Array Based on Multi-Strategy Fusion Improved Adaptive Mayfly Algorithm in Bearing-Only Sensor Network

Layout of Detection Array Based on Multi-Strategy Fusion Improved Adaptive Mayfly Algorithm in Bearing-Only Sensor Network

BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation

Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements

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