Multisensor data fusion using Elman neural networks

Krzysztof Kolanowski, Aleksandra Świetlicka, Rafał Kapela, Janusz Pochmara and Andrzej Rybarczyk

Applied Mathematics and Computation, 2018, vol. 319, issue C, 236-244

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.

Keywords: AHRS; IMU; Sensor fusion; Neural network; Inertial navigation (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:319:y:2018:i:c:p:236-244

DOI: 10.1016/j.amc.2017.02.031

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