Three dimensional target tracking via Underwater Acoustic Wireless Sensor Network

Dehnavi, Saeid; Ayati, Moosa; Zakerzadeh, Mohammad Reza

doi:10.1109/rios.2017.7956459

Cited by 19 publications

(7 citation statements)

References 6 publications

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“…However, due to the problems of poor definition and contrast of underwater images, color distortion, complex background environment, and obscured objects in underwater target tracking, the effect of this method is very poor. Denhavi [9] proposed a three-dimensional target tracking algorithm that uses extended Kalman filter and unscented Kalman filter to filter the measurement noise and estimate the target path. The simulation results show that the filtered path matches the real path, but its speed and accuracy were poor.…”

Section: Introductionmentioning

confidence: 99%

An Underwater Single Target Tracking Method Using SiamRPN++ Based on Inverted Residual Bottleneck Block

Wang

Fan

2021

IEEE Access

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Traditional tracking algorithms reply on manually extracted features while deep learning algorithms can automatically extract features, which is of great importance for single target tracking in the complex underwater environment that is characterized by poor visibility, low contrasts, and occlusion. The convolutional neural network SiamRPN++ is an advanced deep learning algorithm whose backbone network structure is. However, this algorithm is difficult to be implemented in an underwater platform due to its large amount of computation and high requirement for the computing power of the hardware. To solve this problem, a new backbone network structure NewNet-62 characterized by inverted residual bottleneck block and the SiamRPN++ algorithm are proposed. The depth-wise separable convolution in NewNet-62 simplifies the computation so that the SiamRPN++ algorithm can realize fast and accurate tracking of underwater single targets. The video set in VOT2016 was used to test the algorithm. The results show that the SiamRPN++ algorithm using NewNet-62 as the main network has the great performance. Compared with the algorithms using ResNet-50, the accuracy of the SiamRPN++ algorithm increased to 0.524 and its EAO increased to 0.303. And its tracking speed increased to 73.74. The network complexity reduced to 3.013 billion and the network parameters reduced to 12.538 million, which significantly reduces network complexity and number of network parameters.

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Section: Introductionmentioning

confidence: 99%

An Underwater Single Target Tracking Method Using SiamRPN++ Based on Inverted Residual Bottleneck Block

Wang

Fan

2021

IEEE Access

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“…Therefore, the algorithm is not applicable to underwater WSNs. Dehnavi et al proposed a three-dimensional underwater target tracking algorithm [7], and the extended Kalman filter and Unscented Kalman filter were used to estimate the target path by filtering measurement noise. A local node selection (LNS) scheme was proposed in [8], and the distributed Kalman filter with feedback was used to perform tracking.…”

Section: Introductionmentioning

confidence: 99%

Sensor-Networked Underwater Target Tracking Based on Grubbs Criterion and Improved Particle Filter Algorithm

Zhang

Gao

2019

IEEE Access

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For target tracking in underwater wireless sensor networks (WSNs), the contributions of the measured values of each sensor node are different for data fusion, so a better weighted nodes fusion and participation planning mechanism can obtain better tracking performance. A distributed particle filter based target tracking algorithm with Grubbs criterion and mutual information entropy weighted fusion (GMIEW) is proposed in this paper. The Grubbs criterion is adopted to analyze and verify the information obtained by sensor nodes before the information fusion, and accordingly some interference information or error information can be excluded from the data set. In the process of calculating importance weight in particle filter, dynamic weighting factor is introduced. The mutual information entropy between the measured value of the sensor nodes and the target state is used to reflect the amount of target information provided by sensor nodes, thus a dynamic weighting factor corresponding to each node can be obtained. The simulation results show that the proposed algorithm effectively improves the accuracy of prediction of target tracking system. INDEX TERMSUnderwater wireless sensor networks, target tracking, particle filtering, Grubbs criterion, mutual information entropy.

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“…Recent years have witnessed a growing interest in the field of marine sciences. Extensive research has been conducted to explore and exploit environmental, ecological, economic, and defense-related aspects/potentials of world’s waters [1,2,3,4,5,6]. Underwater sensor networks (UWSNs) play a pivotal role in such studies by providing basic technical support for gathering, organizing, and reporting mission-critical data [7].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Localization Scheme with Partition Handling for Data Tagging in Underwater Acoustic Sensor Networks

Islam

Lee

2019

Sensors

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Knowledge about the geographic coordinates of underwater sensor nodes is of primary importance for many applications and protocols of under water sensor networks (UWSNs) thus making localization of sensor nodes a crucial part of underwater network design. In case of mobile underwater sensor network, location estimation becomes challenging not only due to the need for periodic tracking of nodes, but also due to network partitioning caused by the pseudo-random mobility of nodes. Our proposed technique accomplishes the task of localization in two stages: (1) relative localization of sensor nodes with respect to a reference node at regular intervals during sensing operation. (2) Offline absolute localization of sensor nodes using absolute coordinates of the reference node and relative locations estimated during stage 1. As our protocol deals with mobile underwater sensor networks that may introduce network partitioning, we also propose a partition handling routine to deal with network partitions to achieve high localization coverage. The major design goal of our work is to maximize localization coverage while keeping communication overhead minimum, thus achieving better energy efficiency. Major contributions of this paper are: (1) Two energy efficient relative localization techniques, and (2) A partition handling strategy that ensures localization of partitioned nodes.

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Three dimensional target tracking via Underwater Acoustic Wireless Sensor Network

Cited by 19 publications

References 6 publications

An Underwater Single Target Tracking Method Using SiamRPN++ Based on Inverted Residual Bottleneck Block

An Underwater Single Target Tracking Method Using SiamRPN++ Based on Inverted Residual Bottleneck Block

Sensor-Networked Underwater Target Tracking Based on Grubbs Criterion and Improved Particle Filter Algorithm

A Two-Stage Localization Scheme with Partition Handling for Data Tagging in Underwater Acoustic Sensor Networks

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