Skip to main content
SpringerLink
Log in

Metaheuristic Algorithms for Integrated Navigation Systems

  • Chapter
  • First Online:
Computational Intelligence for Unmanned Aerial Vehicles Communication Networks

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1033))

Abstract

This research evaluates novel and powerful metaheuristic optimization approaches for designing integrated navigation systems. For this purpose, Inclined Planes system Optimization (IPO) alongside its modified version called MIPO is used for the first time. Implementations are done on an Inertial Navigation System (INS) integrated with a Global Navigation Satellite System (GNSS). Noise covariance matrices are considered as design variables and the sum of root-mean-squared errors as an objective function in the form of a single-objective optimization problem. Simulation results are reported in terms of all algorithmic and navigation performance indicators. The overall assessment in comparison with two well-known competitors of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) indicates the success of the proposed metaheuristic algorithms over the basic integrated navigation problem.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. G. Minkler, J. Minkler, Theory and Applications of Kalman filtering (Magellan Book Company, 1993)

    Google Scholar 

  2. V. Sathiya, M. Chinnadurai, Evolutionary algorithms-based multi-objective optimal mobile robot trajectory planning. Robotica 37, 1363–1382 (2019)

    Article  Google Scholar 

  3. S.M.J. Jalali, A. Khosravi, P.M. Kebria, R. Hedjam, S. Nahavandi, Autonomous robot navigation system using the evolutionary multi-verse optimizer algorithm, in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (IEEE, 2019), pp. 1221–1226

    Google Scholar 

  4. Q. Liu, Y. Li, L. Liu, A 3D simulation environment and navigation approach for robot navigation via deep reinforcement learning in dense pedestrian environment, in 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) (IEEE, 2020), pp. 1514–1519

    Google Scholar 

  5. L. Cong, S. Yue, H. Qin, B. Li, J. Yao, Implementation of a MEMS-Based GNSS/INS integrated scheme using supported vector machine for land vehicle navigation. IEEE Sens. J. 20, 14423–14435 (2020)

    Article  Google Scholar 

  6. J.-H. Yi, M. Lu, X.-J. Zhao, Quantum inspired monarch butterfly optimisation for UCAV path planning navigation problem. Int. J. Bio-Inspired Comput. 15, 75–89 (2020)

    Article  Google Scholar 

  7. M.G. Bellemare et al., Autonomous navigation of stratospheric balloons using reinforcement learning. Nature 588, 77–82 (2020)

    Article  Google Scholar 

  8. N. Al Bitar, A.I. Gavrilov, Neural networks aided unscented Kalman filter for integrated INS/GNSS systems, in 2020 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS) (IEEE, 2020), pp. 1–4

    Google Scholar 

  9. J. Wang, Z. Ma, X. Chen, Generalized dynamic fuzzy NN model based on multiple fading factors SCKF and its application in integrated navigation. IEEE Sens. J. 21, 3680–3693 (2021)

    Article  Google Scholar 

  10. F. Gul et al., Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using PSO–GWO optimization algorithm with evolutionary programming. J. Ambient Intell. Humaniz. Comput. 12, 7873–7890 (2021)

    Article  Google Scholar 

  11. P. Zieliński, U. Markowska-Kaczmar, 3D robotic navigation using a vision-based deep reinforcement learning model. Appl. Soft Comput. 110, 107602 (2021)

    Google Scholar 

  12. D. Gao, X. Lyu, F. Qin, L. Chang, B. Hu, A real time gravity compensation method for high precision INS based on neural network, in 2021 28th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS) (IEEE, 2021), pp. 1–5

    Google Scholar 

  13. S. Wen, Z. Wen, D. Zhang, H. Zhang, T. Wang, A multi-robot path-planning algorithm for autonomous navigation using meta-reinforcement learning based on transfer learning. Appl. Soft Comput. 110, 107605 (2021)

    Google Scholar 

  14. N. Al Bitar, A. Gavrilov, A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems. Navigation 68, 521–539 (2021)

    Article  Google Scholar 

  15. F. Yan, S. Li, E. Zhang, J. Guo, Q. Chen, An adaptive nonlinear filter for integrated navigation systems using deep neural networks. Neurocomputing 446, 130–144 (2021)

    Article  Google Scholar 

  16. Y. Wu, A survey on population-based meta-heuristic algorithms for motion planning of aircraft. Swarm Evol. Comput. 62, 100844 (2021)

    Google Scholar 

  17. E.P. Herrera, H. Kaufmann, Adaptive methods of Kalman filtering for personal positioning systems, in Proceedings of the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2010) (2010), pp. 584–589

    Google Scholar 

  18. R. Gonzalez, J.I. Giribet, H.D. Patino, NaveGo: a simulation framework for low-cost integrated navigation systems. J. Control Eng. Appl. Inform. 17, 110–120 (2015)

    Google Scholar 

  19. J.F.W. Georgy, Advanced nonlinear techniques for low cost land vehicle navigation. Queen’s University (2010)

    Google Scholar 

  20. K. Abdul Rahim, Heading drift mitigation for low-cost inertial pedestrian navigation. University of Nottingham (2012)

    Google Scholar 

  21. J.H. Holland, Adaptation in Natural and Artificial Systems (The University of Michigan Press, 1975)

    Google Scholar 

  22. D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley Publishing Company, 1989)

    Google Scholar 

  23. M. Dorigo, V. Maniezzo, A. Colorni, Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26, 29–41 (1996)

    Google Scholar 

  24. J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4 (IEEE, 1995), pp. 1942–1948

    Google Scholar 

  25. S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing. Science 220, 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  26. E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)

    Article  Google Scholar 

  27. R. Storn, K. Price, Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  28. K. Price, R.M. Storn, J.A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization (Springer, Berlin, 2005)

    Google Scholar 

  29. S. Das, P.N. Suganthan, Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15, 4–31 (2011)

    Article  Google Scholar 

  30. S. Mirjalili, S.M. Mirjalili, A. Lewis, Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  31. M.H. Mozaffari, H. Abdy, S.H. Zahiri, IPO: an inclined planes system optimization algorithm. Comput. Inform. 35, 222–240 (2016)

    MathSciNet  MATH  Google Scholar 

  32. A. Mohammadi, S.H. Zahiri, IIR model identification using a modified inclined planes system optimization algorithm. Artif. Intell. Rev. 48, 237–259 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Ali Mohammadi & Farid Sheikholeslam

  2. Department of Electrical and Computer Engineering, Yazd University, Yazd, 89158-18411, Iran

    Mehdi Emami

Authors
  1. Ali Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farid Sheikholeslam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mehdi Emami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Mohammadi .

Editor information

Editors and Affiliations

  1. Universite Moulay Ismail De Meknes, Meknes, Morocco

    Mariya Ouaissa

  2. Isra University, Islamabad, Pakistan

    Inam Ullah Khan

  3. Moulay Ismail University, Meknes, Morocco

    Mariyam Ouaissa

  4. Mohammedia, Faculte des Sciences et Techniques, El Mansouria, Morocco

    Zakaria Boulouard

  5. Dalian University of Technology, Dalian, China

    Syed Bilal Hussain Shah

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mohammadi, A., Sheikholeslam, F., Emami, M. (2022). Metaheuristic Algorithms for Integrated Navigation Systems. In: Ouaissa, M., Khan, I.U., Ouaissa, M., Boulouard, Z., Hussain Shah, S.B. (eds) Computational Intelligence for Unmanned Aerial Vehicles Communication Networks. Studies in Computational Intelligence, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-97113-7_4

Download citation

Publish with us

Policies and ethics

Search

Navigation