Radar/ESM anti‐bias track association algorithm based on track distance vector detection

Li, Baozhu; Dong, Yunlong; Huang, Gaodong; Chen, Xiaolong; Guan, Jian

doi:10.1049/joe.2019.0647

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These track correlation algorithms above are mainly aimed at the situation where the number of targets is small and the error is small [7][8][9][10][11]. However, when facing UAV swarm targets, the correlation effect seriously decreases.…”

Section: Introductionmentioning

confidence: 99%

Track Correlation Algorithm Based on CNN-LSTM for Swarm Targets

Chen,

Wang,

Chen

2024

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

The rapid development of unmanned aerial vehicle (UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation. In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets. Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM) Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation, while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

show abstract

Section: Introductionmentioning

confidence: 99%

Track Correlation Algorithm Based on CNN-LSTM for Swarm Targets

Chen,

Wang,

Chen

2024

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

show abstract

“…The first category can also be divided into two parts according to the starting time of different sampling periods. In both categories, asynchronous information from multi-sensor systems can be synchronized by track pretreatment [5] − [7], and then be tracked by traditional synchronous track fusion system. However, these pretreatment process schemes will cause some errors increasing and reduce the reliability of data fusion system.…”

Section: Introductionmentioning

confidence: 99%

An Asynchronous Data Fusion Algorithm for Target Detection Based on Multi-Sensor Networks

Zhang

Wang²,

Guo

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The time interval of the observational data changes irregularly because of the difference of sensors' sampling rate, the communication delay and the target leaving observation region of the sensor sometimes. These problems of asynchronous observation data greatly reduce the tracking accuracy of the multi-sensors system. Therefore, asynchronous data fusion system is more practical than synchronous data fusion system, and worthier of study. By establishing an asynchronous track fusion model with irregular time interval of observation data and combining with the Track Quality with Multiple Model (TQMM), an asynchronous track fusion algorithm with information feedback is proposed, and the TQMM is used for weight allocation to improve the performance of the asynchronous multi-sensor fusion system. The simulation result shows that the algorithm has better tracking performance compared with other algorithms, so that this kind of problem of track-to-track fusion for asynchronous sensors is solved effectively. INDEX TERMS Asynchronous fusion, multi sensors, track fusion, track quality with multiple model.

show abstract

Multi-Sensor Fusion Tracking Algorithm Based on Augmented Reality System

Wang

2021

IEEE Sensors J.

View full text Add to dashboard Cite

Radar/ESM anti‐bias track association algorithm based on track distance vector detection

Cited by 3 publications

References 11 publications

Track Correlation Algorithm Based on CNN-LSTM for Swarm Targets

Track Correlation Algorithm Based on CNN-LSTM for Swarm Targets

An Asynchronous Data Fusion Algorithm for Target Detection Based on Multi-Sensor Networks

Multi-Sensor Fusion Tracking Algorithm Based on Augmented Reality System

Contact Info

Product

Resources

About