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Application of Improved BP Neural Network in Information Fusion Kalman Filter

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Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

Based on improved back propagation (BP) neural network, information fusion state estimation problem for multi-sensor system is considered. Firstly, particle swarm optimization, search dynamic learning rate and additional momentum method are introduced to train the initial weights and thresholds of BP neural network. Then, the improved neural network is used to optimize the estimated value of Kalman filter. Finally, the sate estimators are fused by weighting matrices. A simulation example verifies the effectiveness of the proposed algorithm.

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Funding

This work was supported by the National Natural Science Foundation of China (61573132).

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Authors and Affiliations

  1. Electronic Engineering College, Heilongjiang University, Harbin, People’s Republic of China

    Yu-Hang Yang & Ying Shi

  2. Key Laboratory of Information Fusion Estimation and Detection, Heilongjiang Province, Harbin, People’s Republic of China

    Yu-Hang Yang & Ying Shi

Authors
  1. Yu-Hang Yang
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  2. Ying Shi
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Correspondence to Ying Shi.

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Yang, YH., Shi, Y. Application of Improved BP Neural Network in Information Fusion Kalman Filter. Circuits Syst Signal Process 39, 4890–4902 (2020). https://doi.org/10.1007/s00034-020-01393-y

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  • DOI: https://doi.org/10.1007/s00034-020-01393-y

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