Noise‐aware manoeuvring target tracking algorithm in wireless sensor networks by a novel adaptive cubature Kalman filter

Fang, Xuming; Chen, Lijun

doi:10.1049/iet-rsn.2020.0165

Cited by 8 publications

(5 citation statements)

References 49 publications

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“…The results of behavior recognition cannot be accurately captured when the target motion is deformed. One after another, the method of using correlation over filtering to calculate target tracking and recognition has gradually become the mainstream mode [ 11 ]. In this way, the information data is combined with the tracking model for the first time, and the correlation filter is used to detect the responsiveness in the frame number of the behavior of the moving person, and the feature points of the data are captured.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

Gao

2021

Computational Intelligence and Neuroscience

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With the rapid development of deep learning algorithms, it is gradually applied in UAV (Unmanned Aerial Vehicle) driving, visual recognition, target tracking, behavior recognition, and other fields. In the field of sports, many scientists put forward the research of target tracking and recognition technology based on deep learning algorithms for athletes’ trajectory and behavior capture. Based on the target tracking algorithm, a regional proposal network RPN algorithm combined with the twin regional proposal network Siamese algorithm is proposed to study the tracking and recognition technology of athletes’ behavior. Then, the adaptive updating network is used to track the behavior target of athletes, and the simulation model of behavior recognition is established. This algorithm is different from the traditional twin network algorithm. It can accurately take the athlete’s behavior as the target candidate box in model training and reduce the interference of environment and other factors on model recognition. The results show that the Siamese-RPN algorithm can reduce the interference from the background and environment when tracking the athletes’ target behavior trajectory. This algorithm can improve the training behavior recognition model, ignore the background interference elements of the behavior image, and improve the accuracy and overall performance of the model. Compared with the traditional twin network method for sports behavior recognition, the Siamese-RPN algorithm studied in this paper can perform offline operations and distinguish the interference factors of athletes’ background environment. It can quickly capture the characteristic points of athletes’ behavior as the data input of the tracking model, so it has excellent popularization and application value.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

Gao

2021

Computational Intelligence and Neuroscience

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“…, j ctr j ctr xy represent the center position of the j -th moving target in adjacent images [17][18]. The target tracking algorithm is not only related to the position and speed of the target, but also to changes in the environment.…”

Section:  mentioning

confidence: 99%

Improved Drosophila Visual Neural Network Application in Vehicle Target Tracking and Collision Warning

2023

IJACSA

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To enable the vehicle tracking and collision warning system to face more complex road information, the Drosophila visual neural network collision warning algorithm has been improved, including image stabilization algorithm, target region synthesis algorithm, and target tracking algorithm. The results showed that the improved image stabilization algorithm had significantly higher image stabilization quality. The peak signal-to-noise ratio of the stabilized image before improvement was the highest at 80dB and the lowest at 54dB. After improvement, the peak signal-to-noise ratio of the stabilized image was the highest at 82dB and the lowest at 60dB. The improved algorithm did not have any false alarms or missed alarms in collision warning. In video 1, there were false alarms in the unimproved algorithm, while in video 2, there were missed alarms. In video 1, all frames were in a safe state, but the original algorithm displayed an alarm in frames 7-12, 13-22, and 23-31. In video 2, there were dangerous situations in frames 8-24 that required an alarm, while the original algorithm displayed an alarm message in frames 8-17, consistent with the actual situation. The improved target tracking algorithm can complete the task of extracting target motion curves. The target tracking algorithm extracted the motion curves of one target in video 1 and two targets in video 2, which were consistent with the video content. The improvement of the Drosophila visual neural network collision warning model through research is effective, which can improve the driving safety of vehicles in complex road conditions.

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“…Despite the many superior features of wireless sensor networks, they still face many security issues, for example, battery-powered sensor nodes are not rechargeable and difficult to replace due to cost constraints, so if a large number of nodes die from premature energy depletion, it can lead to serious damage to the network structure, thus affecting the performance and survival time of the network [12]. Fang et al proposed an autonomous in-transit detection system, which integrates RFID and wireless sensor network; the container is full of vegetables and fruits, in which several wireless sensor nodes for detecting ethylene are deployed; the wireless sensor nodes are responsible for collecting the concentration of ethylene, and RFID technology is used to control and record the loading and unloading process of vegetables and fruits, and each time the wireless sensor nodes collect the logistics information, they send to the backend server for processing [13]. Liu et al proposed a wireless sensor network-based intelligent monitoring and tracking system for agricultural logistics transportation equipment, which established a wireless sensor network for monitoring the internal parameters of refrigerated containers and connected to the backend server through an intelligent terminal and a wireless mobile network to transmit the logistics data [14].…”

Section: Related Workmentioning

confidence: 99%

Using Wireless Sensor Network to Remote Real‐Time Monitoring and Tracking of Logistics Status Based on Difference Transmission Algorithm

Ding

2021

Journal of Sensors

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This paper proposes a logistics tracking information management system based on a wireless sensor network, using wireless sensor nodes to track and manage logistics tracking information, and designs a networked logistics tracking information management system to achieve remote real-time management of logistics tracking information. The research work in this paper can mention the real-time and accuracy of logistics tracking information management, which has significant guiding significance to the information security of the logistics industry. This paper first analyzes the business process of logistics enterprises, obtains the demand analysis of logistics tracking management system, divides the whole logistics tracking management system into several functional modules according to the logistics business process of enterprises, analyzes and designs these system functional modules, and gives the model of system functions. After analyzing and designing the system, for the data processing problem in the wireless sensor network, the difference transmission algorithm based on the mean expectation is proposed, which calculates the mean value as the expected value under the specific data model. Then, the data of the difference between the collected value and the expected value is transmitted to reduce the amount of data sent by the nodes, reduce energy consumption, extend the network life cycle, and ensure the stability of the logistics tracking information system. The purpose is to reduce the amount of data sent by nodes, reduce energy consumption, extend the network life cycle, and ensure the stable operation of the logistics tracking information system. In this paper, we describe the logistics tracking system based on a wireless sensor network, which has not only features such as sharing and flexibility but also has high fault tolerance. The system is designed for functional modules such as procurement management, inventory management, and sales management. The JAVA technology platform is selected for design and development, and the SQL Server database is selected for the backend of the system, and a simple and easy-to-use WEB interface for the user service side of the system is designed to meet the needs of a user-friendly environment. The implementation of the system also makes some theoretical and practical contributions to the design and development of similar logistics tracking information management systems based on wireless sensor networks.

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Noise‐aware manoeuvring target tracking algorithm in wireless sensor networks by a novel adaptive cubature Kalman filter

Cited by 8 publications

References 49 publications

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

Improved Drosophila Visual Neural Network Application in Vehicle Target Tracking and Collision Warning

Using Wireless Sensor Network to Remote Real‐Time Monitoring and Tracking of Logistics Status Based on Difference Transmission Algorithm

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