Adaptive Kalman filter implementation by a neural network scheme for tracking maneuvering targets

1995 ◽  
Author(s):  
Farid Amoozegar ◽  
Malur K. Sundareshan
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Adaptive Kalman Filter ◽  
Maneuvering Targets

scholarly journals Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4900
Author(s):  
Son Pham ◽  
Anh Dinh
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Background Noise ◽  
Thermal Noise ◽  
Absolute Error ◽  
Adaptive Kalman Filter ◽  
Training Time ◽  
Burst Noise ◽  
The Neural Network ◽  
The Absolute

Noises such as thermal noise, background noise or burst noise can reduce the reliability and confidence of measurement devices. In this work, a recursive and adaptive Kalman filter is proposed to detect and process burst noise or outliers and thermal noise, which are popular in electrical and electronic devices. The Kalman filter and neural network are used to preprocess data of three detectors of a nondispersive thermopile device, which is used to detect and quantify Fusarium spores. The detectors are broadband (1 µm to 20 µm), λ 1 (6.09 ± 0.06 µm) and λ 2 (9.49 ± 0.44 µm) thermopiles. Additionally, an artificial neural network (NN) is applied to process background noise effects. The adaptive and cognitive Kalman Filter helps to improve the training time of the neural network and the absolute error of the thermopile data. Without applying the Kalman filter for λ 1 thermopile, it took 12 min 09 s to train the NN and reach the absolute error of 2.7453 × 104 (n. u.). With the Kalman filter, it took 46 s to train the NN to reach the absolute error of 1.4374 × 104 (n. u.) for λ 1 thermopile. Similarly, to the λ 2 (9.49 ± 0.44 µm) thermopile, the training improved from 9 min 13 s to 1 min and the absolute error of 2.3999 × 105 (n. u.) to the absolute error of 1.76485 × 105 (n. u.) respectively. The three-thermopile system has proven that it can improve the reliability in detection of Fusarium spores by adding the broadband thermopile. The method developed in this work can be employed for devices that encounter similar noise problems.

Combined estimation of the state of charge of a lithium battery based on a back-propagation– adaptive Kalman filter algorithm

2017 ◽  
Vol 232 (3) ◽  
pp. 357-366 ◽  
Author(s):  
Wanzhong Zhao ◽  
Xiangchuang Kong ◽  
Chunyan Wang
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
The State ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Adaptive Kalman Filter ◽  
Precise Estimation ◽  
Kalman Filter Algorithm

The precise estimation of the battery’s state of charge is one of the most significant and difficult techniques for battery management systems. In order to improve the accuracy of estimation of the state of charge, the forgetting-factor recursive least-squares method is used to achieve online identification of the model parameters based on the first-order RC battery model, and a back-propagation neural-network-assisted adaptive Kalman filter algorithm is proposed. A back-propagation neural network is established by using the MATLAB neural network toolbox and is trained offline on the basis of the battery test data; then the trained back-propagation neural network is used to realize the online optimized results of an adaptive Kalman filter algorithm for estimation of the state of charge. The proposed methodology for estimation of the state of charge is demonstrated using experimental lithium-ion battery module data in dynamic stress tests. The results indicate that, in comparison with the common adaptive Kalman filter algorithm, the back-propagation–adaptive Kalman filter algorithm significantly improved precise estimation of the state of charge.

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