A Reverse Bearings Only Target Motion Analysis (BO-TMA) for improving AUV navigation accuracy

Alexandri, Talmon; Diamant, Roee

doi:10.1109/wpnc.2016.7822842

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…In Bearings-only tracking (BOT), the major goal is to efficiently estimate the dynamics of the single or multi moving targets by means of noise-corrupted passive measurements from array elements localized on observation platforms [ 10 ]. In recent years, BOT has gotten a lot of interest because of its significance and wide uses in a variety of practical applications like aircraft surveillance [ 11 ], underwater SONAR tracking [ 12 ], navigation [ 13 ], passive target tracking [ 14 ], etc., in which a real-time state of object is found by using only the bearings’ passive measurements.…”

Section: Introductionmentioning

confidence: 99%

Application of Spherical-Radial Cubature Bayesian Filtering and Smoothing in Bearings Only Passive Target Tracking

Ali

Chen

et al. 2019

Entropy

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In this paper, an application of spherical radial cubature Bayesian filtering and smoothing algorithms is presented to solve a typical underwater bearings only passive target tracking problem effectively. Generally, passive target tracking problems in the ocean environment are represented with the state-space model having linear system dynamics merged with nonlinear passive measurements, and the system is analyzed with nonlinear filtering algorithms. In the present scheme, an application of spherical radial cubature Bayesian filtering and smoothing is efficiently investigated for accurate state estimation of a far-field moving target in complex ocean environments. The nonlinear model of a Kalman filter based on a Spherical Radial Cubature Kalman Filter (SRCKF) and discrete-time Kalman smoother known as a Spherical Radial Cubature Rauch–Tung–Striebel (SRCRTS) smoother are applied for tracking the semi-curved and curved trajectory of a moving object. The worth of spherical radial cubature Bayesian filtering and smoothing algorithms is validated by comparing with a conventional Unscented Kalman Filter (UKF) and an Unscented Rauch–Tung–Striebel (URTS) smoother. Performance analysis of these techniques is performed for white Gaussian measured noise variations, which is a significant factor in passive target tracking, while the Bearings Only Tracking (BOT) technology is used for modeling of a passive target tracking framework. Simulations based experiments are executed for obtaining least Root Mean Square Error (RMSE) among a true and estimated position of a moving target at every time instant in Cartesian coordinates. Numerical results endorsed the validation of SRCKF and SRCRTS smoothers with better convergence and accuracy rates than that of UKF and URTS for each scenario of passive target tracking problem.

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Section: Introductionmentioning

confidence: 99%

Application of Spherical-Radial Cubature Bayesian Filtering and Smoothing in Bearings Only Passive Target Tracking

Ali

Chen

et al. 2019

Entropy

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“…Various underwater robots have emerged as the times have required [8]. As a sort of subaqueous operation device, autonomous underwater vehicles (AUVs) can complete underwater operations under the condition of unmanned operation, and can fulfill the tasks of environmental detection, target recognition, and so on [9,10,11]. With the continuous development of related technologies and theories in recent decades, AUV technology is constantly upgrading, and its application to complete underwater detection is low-cost and highly-efficient.…”

Section: Introductionmentioning

confidence: 99%

Clustering Cloud-Like Model-Based Targets Underwater Tracking for AUVs

Sheng

Tang

Qin

et al. 2019

Sensors

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Autonomous underwater vehicles (AUVs) rely on a mechanically scanned imaging sonar that is fixedly mounted on AUVs for underwater target barrier-avoiding and tracking. When underwater targets cross or approach each other, AUVs sometimes fail to track, or follow the wrong target because of the incorrect association of the multi-target. Therefore, a tracking method adopting the cloud-like model data association algorithm is presented in order to track underwater multiple targets. The clustering cloud-like model (CCM) not only combines the fuzziness and randomness of the qualitative concept, but also achieves the conversion of the quantitative values. Additionally, the nearest neighbor algorithm is also involved in finding the cluster center paired to each target trajectory, and the hardware architecture of AUVs is proposed. A sea trial adopting a mechanically scanned imaging sonar fixedly mounted on an AUV is carried out in order to verify the effectiveness of the proposed algorithm. Experiment results demonstrate that compared with the joint probabilistic data association (JPDA) and near neighbor data association (NNDA) algorithms, the new algorithm has the characteristic of more accurate clustering.

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“…Target motion analysis is mainly used to estimate the motion state of the target, such as position and velocity, by using the continuous azimuth information or azimuth and frequency information obtained from the observation on the single array or multi-array platform [1][2][3].In the bearing-only Target Motion Analysis (BO-TMA) problem with single array, there is a nonlinear relationship between Bearing measurements and target state variables [4][5][6], so the most commonly used methods are batch estimation method and recursive Bayesian estimation method.The research object of this paper is the uniform linear target and the maneuvering target with low speed and slow steering. The batch estimation method can provide reference for the motion analysis of the uniform linear target, and the recursive Bayesian estimation method can provide reference for the motion analysis of the maneuvering target with low speed and slow steering.…”

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confidence: 99%

Pseudolinear Kalman filter algorithms for single array bearings-only target motion analysis

Su¹

2023

Third International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023)

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In the research of target motion analysis, the target motion is usually approximated as a uniform linear target in most cases. However, in the actual environment, the target motion is usually a non-uniform linear motion and the target motion trajectory is mostly a curve, so it is particularly important to estimate the target state under this motion situation. In this paper, the motion analysis of low-speed and slow-turning targets is studied. Interested in low-speed and slow-maneuvering moving targets less than 6 knots, the deviation compensation algorithm of pseudo-linear Kalman filter in two-dimensional space and three-dimensional space is derived. Finally, the performance of the algorithm is verified by simulation, and the influence of measurement error and initial distance on the performance of the algorithm is studied.

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A Reverse Bearings Only Target Motion Analysis (BO-TMA) for improving AUV navigation accuracy

Cited by 5 publications

References 12 publications

Application of Spherical-Radial Cubature Bayesian Filtering and Smoothing in Bearings Only Passive Target Tracking

Application of Spherical-Radial Cubature Bayesian Filtering and Smoothing in Bearings Only Passive Target Tracking

Clustering Cloud-Like Model-Based Targets Underwater Tracking for AUVs

Pseudolinear Kalman filter algorithms for single array bearings-only target motion analysis

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