Simulation of platform-free inertial navigation system of unmanned aerial vehicles based on neural network algorithms

Authors

DOI:

https://doi.org/10.15587/2706-5448.2021.225282

Keywords:

neural network, flight trajectory, neural network learning accuracy, simulation, navigation data

Abstract

The object of research is the process of controlling the trajectory of unmanned aerial vehicles (UAVs) in autonomous flight mode based on neural network algorithms. The study is based on the application of numerical-analytical approach to the selection of modern technical solutions for the construction of standard models of platformless inertial navigation systems (BINS) for micro and small UAVs, followed by support for assumptions. The results of simulation in the Matlab environment allowed to simulate the operation of the UAV control system based on MEMS technology (using microelectromechanical systems) and Arduino microcomputers. It was also possible to experimentally determine the nature of the influence of the structure of the selected neural network on the process of formation of navigation data during the disappearance of the GPS signal. Thus, to evaluate the effectiveness of the proposed solutions for the construction of BINS, a comparative analysis of the application of two algorithms ELM (Extreme Learning Machine)-Kalman and WANN (Wavelet Artificial Neural Network)-RNN (Recurrent Neural Network)-Madgwick in the form of two experiments. The purpose of the experiments was to determine: the study of the influence of the number of neurons of the latent level of the neural network on the accuracy of approximation of navigation data; determining the speed of the process of adaptive learning of neural network algorithms BINS UAV. The results of the experiments showed that the application of the algorithm based on ELM-Kalman provides better accuracy of learning the BINS neural network compared to the WANN-RNN-Madgwick algorithm. However, it should be noted that the accuracy of learning improved with the number of neurons in the structure of the latent level <500, which iincreases computational complexity and increases the learning process time. This can complicate the practical implementation using micro- and small UAV equipment. In addition, thanks to the simulation, the result of the study of the application of the proposed neural network algorithms to replace the input data instead of GPS signals to the input BINS, allowed to estimate the positioning error during the disappearance of GPS signals. Also, the application of the WANN-RNN-Madgwick algorithm allows to approximate and extrapolate the input signals of navigation parameters in a dynamic environment, while the process of adaptive learning in real time.

Author Biography

Robert Bieliakov, Military Institute of Telecommunication and Information Technologies named after the Heroes of Kruty

Кандидат технічних наук

Кафедра технічного та метрологічного забезпечення

References

  1. Santoso, F., Garratt, M. A., Anavatti, S. G. (2018). State-of-the-Art Intelligent Flight Control Systems in Unmanned Aerial Vehicles. IEEE Transactions on Automation Science and Engineering, 15 (2), 613–627. doi: http://doi.org/10.1109/tase.2017.2651109
  2. Zhou, Y., Wan, J., Li, Z., Song, Z. (2017). GPS/INS integrated navigation with BP neural network and Kalman filter. 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO). doi: http://doi.org/10.1109/robio.2017.8324798
  3. Sun, C., He, W., Ge, W., Chang, C. (2017). Adaptive neural network control of biped robots. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47 (2), 315–326. doi: http://doi.org/10.1109/tsmc.2016.2557223
  4. Veremeenko, K. K., Krasilschikov, M. N., Sypalo, K. A. (2008). Upravlenie i navedenie bespilotnykh manevrennykh letatelnykh apparatov na osnove sovremennykh informatsionnykh tekhnologii. Moscow: Fizmatlit, 211–219.
  5. Ding, S., Ma, G., Shi, Z. (2013). A Rough RBF Neural Network Based on Weighted Regularized Extreme Learning Machine. Neural Processing Letters, 40 (3), 245–260. doi: http://doi.org/10.1007/s11063-013-9326-5
  6. Niu, X., Nassar, S., El-Sheimy, N. (2007). An Accurate Land-Vehicle MEMS IMU/GPS Navigation System Using 3D Auxiliary Velocity Updates. Navigation, 54 (3), 177–188. doi: http://doi.org/10.1002/j.2161-4296.2007.tb00403.x
  7. Fesenko, O., Bieliakov, R., Radzivilov, G., Huliy, V. (2020). Experimental analysis of possibilities of application of neural networks for a management by the trajectory of flight of UAV. Zbirnyk naukovykh prats VITI, 1, 97–112. Available at: http://www.viti.edu.ua/files/zbk/2020/11_1_2020.pdf
  8. Tikhonov, V. A. (2004). Neirosetevaia model algoritma besplatformennoi inertsialnoi navigatsionnoi sistem. Aerokosmicheskie pribornye tekhnologii, 47–50.
  9. Fakharian, A., Gustafsson, T., Mehrfam, M. (2011). Adaptive Kalman filtering based navigation: An IMU/GPS integration approach. 2011 International Conference on Networking, Sensing and Control, 181–185. doi: http://doi.org/10.1109/icnsc.2011.5874871
  10. Jiang, C., Chen, S., Chen, Y., Zhang, B., Feng, Z., Zhou, H., Bo, Y. (2018). A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN). Sensors, 18 (10), 3470. doi: http://doi.org/10.3390/s18103470
  11. Gross, J., Gu, Y., Gururajan, S., Seanor, B., Napolitano, M. (2010). A Comparison of Extended Kalman Filter, Sigma-Point Kalman Filter, and Particle Filter in GPS/INS Sensor Fusion. AIAA Guidance, Navigation, and Control Conference. doi: http://doi.org/10.2514/6.2010-8332
  12. Ning, Y., Wang, J., Han, H., Tan, X., Liu, T. (2018). An Optimal Radial Basis Function Neural Network Enhanced Adaptive Robust Kalman Filter for GNSS/INS Integrated Systems in Complex Urban Areas. Sensors, 18 (9), 3091. doi: http://doi.org/10.3390/s18093091
  13. Thomas, E., Hendrik, M. J., Frank, H. (2019). Neural architecture search: A Survey. Journal of Machine Learning Research, 20 (55), 1–21. Available at: https://www.jmlr.org/papers/volume20/18-598/18-598.pdf
  14. Gaier, A., Ha, D. (2019). Weight agnostic neural networks. Available at: https://arxiv.org/abs/1906.04358
  15. Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y. (2018). Quantized neural networks: training neural networks with low precision weights and activations. Journal of Machine Learning Research, 18, 1–30. View at: https://jmlr.org/papers/v18/16-456.html
  16. Lashley, M., Bevly, D. M., Hung, J. Y. (2009). Performance Analysis of Vector Tracking Algorithms for Weak GPS Signals in High Dynamics. IEEE Journal of Selected Topics in Signal Processing, 3 (4), 661–673. doi: http://doi.org/10.1109/jstsp.2009.2023341
  17. Cao, Y., Jia, F., Jiang, X., Zhang, Q. (2019). Prediction of SINS/GPS Navigation Information by ELM Algorithm during GPS outages. IOP Conference Series: Materials Science and Engineering, 612, 032002. doi: http://doi.org/10.1088/1757-899x/612/3/032002
  18. Zhao, J., Wang, Z., Park, D. S. (2012). Online sequential extreme learning machine with forgetting mechanism. Neurocomputing, 87, 79–89. doi: http://doi.org/10.1016/j.neucom.2012.02.003
  19. Bieliakov, R. O., Radzivilov, H. D., Fesenko, O. D., Vasylchenko, V. V., Tsaturian, O. G., Shyshatskyi, A. V., Romanenko, V. P. (2019). Method of the intelligent system construction of automatic control of unmanned aircraft apparatus. Radio Electronics, Computer Science, Control, 1, 218–229. doi: http://doi.org/10.15588/1607-3274-2019-1-20

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Published

2021-02-28

How to Cite

Bieliakov, R. (2021). Simulation of platform-free inertial navigation system of unmanned aerial vehicles based on neural network algorithms. Technology Audit and Production Reserves, 1(2(57), 15–19. https://doi.org/10.15587/2706-5448.2021.225282

Issue

Vol. 1 No. 2(57) (2021): Information and control systems

Section

Systems and Control Processes: Reports on Research Projects

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Copyright (c) 2021 Роберт Олегович Беляков

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