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Multisensor data fusion using Elman neural networks

Author

Listed:
  • Kolanowski, Krzysztof
  • Świetlicka, Aleksandra
  • Kapela, Rafał
  • Pochmara, Janusz
  • Rybarczyk, Andrzej

Abstract

The paper presents a navigation system based on Elman Artificial Neural Network (ANN). The task of data fusion from different sensors is realized by trained ANN. Determining position in space is an issue of nonlinear hence. Not every type of ANN is used for such a task. Choice of Elman ANN was dictated by its construction and successfully applications to nonlinear problems requiring prediction. Elman network is composed of three layers. Comprises a layer of hidden layer units context which is connected to the hidden layer. Context-sensitive layer allows for store the values of previous hidden units. With this layer prediction is possible in sequential order. This is the effect of contextual memory where information is stored about what it was before. This kind of functionality is not able to provide any other standard neural network unidirectional. The system consists of MEMS (Micro Electro-Mechanical Systems) sensors, which are based on IMU (Inertial Measurement Unit). IMU is composed from gyroscopes, accelerometers and magnetometers which provide three dimensional linear accelerations and angular rates. This is a classic set of sensors for determining the position in space. The study presents the results of the implementation of algorithms for determining the position in space using trained Elman ANN. The data samples to train ANN were collected during the test flight of Quadrocopter. Paper presents the performance for different configurations of Elman ANN. Presented system provides easy addition of other sensors e.g. GPS/GLONASS receiver.

Suggested Citation

  • Kolanowski, Krzysztof & Świetlicka, Aleksandra & Kapela, Rafał & Pochmara, Janusz & Rybarczyk, Andrzej, 2018. "Multisensor data fusion using Elman neural networks," Applied Mathematics and Computation, Elsevier, vol. 319(C), pages 236-244.
    • Handle: RePEc:eee:apmaco:v:319:y:2018:i:c:p:236-244
    DOI: 10.1016/j.amc.2017.02.031
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    Citations

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    Cited by:

    1. Xinyu Wan & Qingyan Yang & Peng Jiang & Ping’an Zhong, 2019. "A Hybrid Model for Real-Time Probabilistic Flood Forecasting Using Elman Neural Network with Heterogeneity of Error Distributions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 4027-4050, September.
    2. Xiaoqin Liu & Xiang Li & Qi Shi & Chuanpei Xu & Yanmei Tang, 2021. "UAV attitude estimation based on MARG and optical flow sensors using gated recurrent unit," International Journal of Distributed Sensor Networks, , vol. 17(4), pages 15501477211, April.

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