Study on the Self-learning Unscented Kalman Filter Algorithm Based on the BP Neural Network

By using unscented kalman filter (UKF) theory and introducing adaptive factor into BP neural network, a new prediction model of concrete dam deformation was proposed. Example shows that this model can improve the convergence speed of BP neural network, and the calculation precision of this model meets engineering requirements. Meanwhile, this model can be applied in the safety monitoring of other hydraulic engineering structure.

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BP neural Network-Kalman filter fusion method for unmanned aerial vehicle target tracking

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406220983864 ◽

2021 ◽

pp. 095440622098386

Author(s):

Yongqi Liu ◽

Liangcheng Nie ◽

Rui Dong ◽

Gang Chen

Keyword(s):

Neural Network ◽

Kalman Filter ◽

Target Tracking ◽

Real Time ◽

Bp Neural Network ◽

Target Position ◽

Difference Matrix ◽

Onboard Computer ◽

Center Position ◽

Kalman Filter Algorithm

The poor real-time performance and target occlusion occurred easily when the UAV was tracking the target. In this paper, a target tracking method based on the Back Propagation neural network fusion Kalman filter algorithm was developed to solve the position prediction problem of the UAV target tracking in real time. Firstly, the target tracking algorithm was used to acquire the center position coordinates of the target on the onboard computer, and then the coordinate difference matrix was constructed to train the BP neural network in real time. Secondly, when the target was occluded by the obstacles judged by the Bhattacharyya coefficient, the BP neural network fusion Kalman filter algorithm was used to accurately predict the center position coordinates of the occluded target. Then the flight speed of UAV was calculated by the deviation between the coordinates of the target and the image center. Finally, the velocity command was sent to the UAV by the onboard computer. The experimental results shown that the target position predicted by BP neural network fusion Kalman filter algorithm was more accurate and robust in predicting the center position coordinates of the target, and the UAV can track the moving target on the ground stably.

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A Low Complexity Extended Kalman Filter Algorithm for Neural Network Digital Predistortion of Power Amplifier

2017 International Conference on Green Informatics (ICGI) ◽

10.1109/icgi.2017.26 ◽

2017 ◽

Cited By ~ 1

Author(s):

Linhuang Wu ◽

Kaixiong Su ◽

Zhifeng Chen ◽

Pingping Chen

Keyword(s):

Neural Network ◽

Kalman Filter ◽

Extended Kalman Filter ◽

Power Amplifier ◽

Low Complexity ◽

Digital Predistortion ◽

Kalman Filter Algorithm

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Study for Workpiece Identity Based on the BP Neural Network and the Zernike Moment

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.694-697.1958 ◽

2013 ◽

Vol 694-697 ◽

pp. 1958-1963 ◽

Cited By ~ 1

Author(s):

Xian Wei ◽

Jing Dong Zhang ◽

Xue Mei Qi

Keyword(s):

Neural Network ◽

Fault Tolerance ◽

Parallel Processing ◽

Bp Neural Network ◽

Complex Shape ◽

Learning Ability ◽

Zernike Moment ◽

Recognition Ability ◽

Identity Based ◽

Self Learning

The robots identify, locate and install the workpiece in FMS system by identifying the characteristic information of target workpiece. The paper studied the recognition technology of complex shape workpiece with combination of BP neural network and Zernike moment. The strong recognition ability of Zernike moment can extract the characteristic. The good fault tolerance, classification, parallel processing and self-learning ability of BP neural network can greatly improve the accurate rate of recognition. Experimental results show the effectiveness of the proposed method.

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