Tracking multiple Autonomous Underwater Vehicles

Melo, Juliana; Matos, Aníbal

doi:10.1007/s10514-018-9696-7

Cited by 12 publications

(8 citation statements)

References 36 publications

(36 reference statements)

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“…The splitting mode referred in [50], [51] is corresponding to the S1 of this paper, here we give the approximation of predicted labeled joint density of split pair derived in [51] (20) where, L ,T,+ denotes the label set split from label .…”

Section: A Ggiw-δ-glmb Filter Based Approaches For Group Splitting Trackingmentioning

confidence: 99%

“…p (ς ) T ξ (i,κ) j,+ , T,i,j,+ denotes the target density generated from the splitting model. To keep consistent with (20) in expression and discriminate the split tracks with their parent, let p…”

Section: A Ggiw-δ-glmb Filter Based Approaches For Group Splitting Trackingmentioning

confidence: 99%

“…If κ is an array, 2d |κ| + 1 possible predicted GGIW components will be generated from one posterior GGIW component, where one from group surviving and prediction and 2d |κ| from group splitting, which lead d |κ| splitting pair for S1 (see (20)) or 2d |κ| splitting pair for S2 or S3 (see (26) and (27), for S3, one splitting pair only contains one GGIW component). If a posterior GLMB component contains n GGIW components, (2d |κ| + 2) n − 1 predicted GLMB components will be generated at most, where target death is contained in 2d |κ|+2, ''−1'' denotes no target exists, and the GLMB RFS is ∅.…”

Section: A Group Splitting Criteriamentioning

confidence: 99%

“…[5]- [8]. Currently, the main approaches for multi-target tracking contains joint probabilistic data association filter(JPDAF) [9]- [12], multiple hypothesis tracking(MHT) [13]- [16] and the approaches based on random finite set (RFS) [17]- [20]. Where, JPDAF, MHT and many other traditional multi-target tracking approaches are the combination of single target filtering and data association in essential, while the RFS based approaches e.g., probability hypothesis density(PHD)…”

Section: Introductionmentioning

confidence: 99%

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Tracking the Splitting and Combination of Group Target With $\delta$ -Generalized Labeled Multi-Bernoulli Filter

Linhai

Wang

2019

IEEE Access

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Splitting and combination are two important events of group target motion. However, the existing tracking approaches for group target splitting and combination events suffer the problems of high-computational cost and low accuracy. Under the random finite set framework, with target extent modeled by random matrix, the algorithms for group target splitting and combination tracking based on δ-generalized labeled multi-Bernoulli filter are researched. Three classical splitting modes of group target are discussed. With appropriate splitting criteria developed, e.g., the setting of the splitting gate, the chosen of the splitting dimension, the compensation of the subgroup's centroid position, and so on. According to the characteristics of each mode, the efficiency and the accuracy of the algorithm for group target splitting event are improved. The group combination approach is derived, where the representation of labels under the tack complicatedly changed condition, e.g., the group splitting and combination events jointly exist are given. With the velocity combination criterion established according to the target motion trend, a decreased sensitivity of the algorithm for target splitting event is avoided. The results show that the proposed algorithms have improved the tracking performance for group target splitting and combination events.INDEX TERMS δ-generalized labeled multi-Bernoulli, group target tracking, splitting, combination, gamma Gaussian inverse Wishart.

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Section: A Ggiw-δ-glmb Filter Based Approaches For Group Splitting Trackingmentioning

confidence: 99%

Section: A Ggiw-δ-glmb Filter Based Approaches For Group Splitting Trackingmentioning

confidence: 99%

Section: A Group Splitting Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tracking the Splitting and Combination of Group Target With $\delta$ -Generalized Labeled Multi-Bernoulli Filter

Linhai

Wang

2019

IEEE Access

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“…In the environment described above, most navigation algorithms that require external navigation data will suffer performance degradation. But Simultaneous Localization and Mapping (SLAM) is better suited to the situation above [1][2][3][4][5][6]. e SLAM method can be described as that the mobile robot starts from a certain location in an unknown environment, acquires the position of the map feature near the environment relative to itself by the sensor carried by the robot itself, and incrementally constructs the map to determine the map information; and correct the robot's own posture based on the determined map information [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information

Zhang

Yang

et al. 2021

Mathematical Problems in Engineering

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The Simultaneous Localization and Mapping (SLAM) method of mobile robots has the problem of low accuracy in complex environments with dense clutter and various map features, such as complex indoor environments and underwater environments. This problem is mainly embodied in estimating the location and number of feature points on the map and the position of the robot itself. In order to solve this problem, a new method based on the probability hypothesis density (PHD) SLAM is proposed in this paper, a PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information (AI-MBMI-PHD-SLAM). Firstly, this paper proposes a PHD-SLAM method based on amplitude information (AI-PHD-SLAM). The method uses the amplitude information of map features to obtain more precise map features. Then, the clutter likelihood function is used to improve the estimation accuracy of the feature map in the SLAM process. Meanwhile, this paper studies the performance of the PHD-SLAM method with the amplitude information under the condition of the known signal-to-noise ratio or the unknown signal-to-noise ratio. Secondly, aiming at the problem that PHD-SLAM lacks a priori information in the prediction stage, an AI-PHD-SLAM-based mixed birth map information method is added. In this method, map information that has been detected before the previous moment is added to the observation information in the map prediction phase as a new map information set in the prediction phase. This can increase the prior information and improve the problem of insufficient prior information in the prediction stage. The results of the experiments show that the proposed method and the improved method outperform the RB-PHD-SLAM method in estimating the number and location accuracy of map features and have higher computational efficiency.

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The Distributed Adaptive Finite-Time Chattering Reduction Containment Control for Multiple Ocean Bottom Flying Nodes

Qin

Chen

Sun

et al. 2019

Int. J. Fuzzy Syst.

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Tracking multiple Autonomous Underwater Vehicles

Cited by 12 publications

References 36 publications

Tracking the Splitting and Combination of Group Target With $\delta$ -Generalized Labeled Multi-Bernoulli Filter

Tracking the Splitting and Combination of Group Target With $\delta$ -Generalized Labeled Multi-Bernoulli Filter

A PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information

The Distributed Adaptive Finite-Time Chattering Reduction Containment Control for Multiple Ocean Bottom Flying Nodes

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