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Performance Analysis of Computational Intelligence Correction

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Abstract

Kalman filter (KF) is a widely used navigation algorithm, especially for precise positioning applications. However, the exact filter parameters must be defined a priori to use standard Kalman filters for co** with low error values. But for the dynamic system model, the covariance of process noise is a priori entirely undefined, which results in difficulties and challenges in the implementation of the conventional Kalman filter. Kalman Filter with recursive covariance estimation applied to solve those complicated functional issues, which can also be used in many other applications involving Kalaman filtering technology, a modified Kalman filter called MKF-RCE. While this is a better approach, KF with SAR tuned covariance has been proposed to resolve the problem of estimation for the dynamic model. The data collected at (x: 706,970.9093 m, y: 6,035,941.0226 m, z: 1,930,009.5821 m) used to illustrate the performance analysis of KF with recursive covariance and KF with computational intelligence correction by means of SAR (Search and Rescue) tuned covariance, when the covariance matrices of process and measurement noises are completely unknown in advance.

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Enquiries about data availability should be directed to the authors.

References

  1. G. Laveti, G. S. Rao, K. J. Rani, A. Nalinee and A. M. Babu, (2014).GPS receiver SPS accuracy assessment using LS and LQ estimators for precise navigation 2014 Annual IEEE India Conference (INDICON), pp. 1–5, Pune,2014. https://doi.org/10.1109/INDICON.2014.7030411.

  2. Tolman, Brian W. (2008) GPS Precise Absolute Positioning via Kalman Filtering Proceedings of the 21st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2008) Savannah, GA, September pp. 1864–1874

  3. Song, J., & Xue, G. (2016). Kang Y A novel method for optimum global positioning system satellite selection based on a modified genetic algorithm. PLoS ONE, 11(3), e0150005. https://doi.org/10.1371/journal.pone.0150005

    Article  Google Scholar 

  4. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Trans. ASME-Journal of Basic Engineering., 82, 35–45.

    Article  MathSciNet  Google Scholar 

  5. Kalman, R. E., & Bucy, R. S. (1961). New results in linear filtering and prediction theory. Trans. ASME Ser. D. Journal of. Basic Engineering., 83, 95–107.

    Article  Google Scholar 

  6. X. **ao, B. Feng, B. Wang, On-line realization of SVM Kalman filter for MEMS gyro, in Proceedings of the 3rd International Conference on Measuring Technology and Mechatronics Automation, pp. 768–770.

  7. Feng, B., Fu, M. Y., Ma, H. B., & **a, Y. (2014). Kalman filter with recursive covariance estimation–sequentially estimating process noise covariance. IEEE Transactions on Industrial Electronics, 61(11), 6253–6263.

    Article  Google Scholar 

  8. Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J. (2009). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2), 239–287.

    Article  MathSciNet  Google Scholar 

  9. Kumar, M., Husian, M., Upreti, N., & Gupta, D. (2010). Genetic algorithm: review and application. International Journal of Information Technology and Knowledge Management, 2(2), 451–454.

    Google Scholar 

  10. J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of the IEEE International Conference on Neural Networks, Perth, WA, Australia, December 1995.

  11. Du, K. L., & Swamy, M. (2016). Ant Colony Optimization. Springer, Berlin, Germany: Search and Optimization by Metaheuristics.

    Google Scholar 

  12. Yunlong, T., & **aopin, C. (2008). Research on Improved Kalman Filter Algorithm for Improving GPS Positioning Accuracy. Modern electronic technology, 3, 4–6.

    Google Scholar 

  13. Shabani, A., Asgarian, B., Gharebaghi, S. A., Salido, M. A., & Giret, A. (2019). A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering, 2019(1), 2482543. https://doi.org/10.1155/2019/2482543

    Article  Google Scholar 

  14. Islam, M. R., & Kim, J. M. (2014). An Effective Approach to Improving Low-Cost GPS Positioning Accuracy in Real-Time Navigation. The Scientific World Journal, 2014(1), 671494. https://doi.org/10.1155/2014/671494

    Article  Google Scholar 

  15. J. Huang and C. Tsai, Improve GPS positioning accuracy with context awareness in Proceedings of the 1st IEEE International Conference on Ubi-Media Computing and Workshops (U-Media ’08) 94–99, Lanzhou, China, JulyAugust 2008.

  16. Specht, C., Pawelski, J., Smolarek, L., Specht, M., & Dabrowski, P. (2019). Assessment of the positioning accuracy of DGPS and EGNOS systems in the Bay of Gdansk using maritime dynamic measurements. Journal of Navigation, 72(3), 575–587. https://doi.org/10.1017/S0373463318000838

    Article  Google Scholar 

  17. Jong-woo An and Jang-Myung Lee, 2017 Improvement of GPS position estimation using SNR and Doppler, 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Munich, 1645–1650 https://doi.org/10.1109/AIM.2017.8014254.

  18. M. Zhao, J. Wang, S. Zhang & C. Zhang (2018) Method for improving positioning accuracy by using double low-precision GPS, 2018 International Symposium in Sensing and Instrumentation in IoT Era, pp. 1-6, Shanghai, https://doi.org/10.1109/ISSI.2018.8538110.

  19. **aohui, Du., & Zhang, R. (2008). Accuracy analysis of GPS static positioning Based on kalman filter. Global Positioning System, 33, 47–51.

    Google Scholar 

  20. Yanfei, L., & Suoli, G. (2011). Application of kalman filter in GPS positioning error processing. Electronic technology, 9, 140–142.

    Google Scholar 

  21. Yong, Z., & Linya, T. (2013). Application research of kalman filter in GPS precise point positioning. Surveying and map** bulletin, 7, 8–15.

    Google Scholar 

  22. Ma H., Yan L., **a Y., Fu M., 2020 Modified Kalman Filter with Recursive Covariance Estimation for Gyroscope Denoising. In: Kalman Filtering and Information Fusion. Springer, Singapore, https://doi.org/10.1007/978-981-15-0806-65.

  23. Badawi, H., Arqub, O. A., & Shawagfeh, N. (2023). Well-posedness and numerical simulations employing Legendre-shifted spectral approach for Caputo-Fabrizio fractional stochastic integrodifferential equations. International Journal of Modern Physics C, 34(06), 2350070. https://doi.org/10.1142/S0129183123500705

    Article  Google Scholar 

  24. Arqub, O. A., & Abo-Hammour, Z. (2014). Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Information sciences, 279, 396–415. https://doi.org/10.1016/j.ins.2014.03.128

    Article  MathSciNet  Google Scholar 

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Acknowledgements

The research has been supported by Ministry of Electronics and Information Technology, Govt of India., under Visvesvaraya PhD Scheme for Electronics and IT.

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Authors and Affiliations

  1. Electronics & Communication Engineering, Dadi Institute of Engineering & Technology(A), Visakhapatnam, Andhra Pradesh, 531001, India

    Nalineekumari Arasavali

  2. Electronics & Communication Engineering, Andhra University College of Engineering (A), Visakhapatnam, India

    Sasibhushana Rao Gottapu

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  1. The authors of the paper Nalineekumari Arasavali and Sasibhushanarao Gottapu are equally contributed.

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    Arasavali, N., Gottapu, S.R. Performance Analysis of Computational Intelligence Correction. Wireless Pers Commun (2024). https://doi.org/10.1007/s11277-024-11399-3

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