Skip to main content
SpringerLink
Log in

Evaluation of DB-IEKF Algorithm Using Optimization Methods for Underwater Passive Target Tracking

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Tracking using only bearing measurements has elemental drawbacks like poor observability of the process and significant preliminary faults. Due to this reason, passive target tracking using bearing and frequency measurements is of good significance for developing a vigorous and swift-tracking system in a passive framework. Centered on the comparative analysis of traditional non-linear target tracking problems, a modern filtering technique called Doppler-Bearing Iterated Extended Kalman Filter (DB-IEKF) is projected in this correspondence. In this research, a new DB-IEKF framework for solving the problem of nonlinear filtering is presented with different optimization methods and compared with Doppler-Bearing Extended Kalman Filter (DBEKF). Besides, new optimization methods are also incorporated in this research to lessen the complexity in the optimization techniques, which reduces the computing complication. DB-IEKF has proved to be a vital tool for estimating the state of the target while tracking using nonlinear systems. The DB-IEKF, on the other hand, does not acquire optimal features that are comparable to those of the extended Kalman filter, and it may perform badly. By considering the DB-IEKF as an optimization problem, it is possible to enhance its efficiency and resilience in a variety of situations. DB-IEKF was carried out using different optimization methods in MATLAB software and proved the performance of each optimization technique concerning DB-IEKF and compared those methods with DBEKF.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

Not applicable.

Code availability

Not applicable.

References

  1. Nardone S, Lindgren AG, Gong K (1984) Fundamental properties and performance of conventional bearings-only target motion analysis. IEEE Trans Autom Control 29(9):775–787

    Article  Google Scholar 

  2. Kumar DR, Rao SK, Raju KP (2017) Estimate-Merge-Technique-based algorithms to track an underwater moving target using towed array bearing-only measurements. Sādhanā 42(9):1617–1628

    Article  MathSciNet  Google Scholar 

  3. Gordon NJ, Salmond DJ, Smith AF (1993, April) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In: IEE proceedings F (radar and signal processing), vol 140, no 2. IET Digital Library, pp. 107-113

  4. Aidala VJ (1979) Kalman Filter Behavior in Bearings-Only Tracking Applications. in IEEE Transactions on Aerospace and Electronic Systems, AES-15(1):29–39

  5. Venkatachalam D (2021) An innovative vehicle surveillance framework using Internet of Things. Int J Innov Sci Eng Res 8(1):26–35

    MathSciNet  Google Scholar 

  6. Satishkumar P, Saravana Murthi C (2019) Soft computing techniques for exhibiting progression constraints in manufacturing process. Int J Innov Sci Eng Res 6(1):1–6

    Google Scholar 

  7. Koteswara Rao S (2021) Bearings-only tracking: observer maneuver recommendation. IETE Journal of Research, 67(2):193–204

  8. Chen Z, Xu W (2018) Joint passive detection and tracking of underwater acoustic target by beamforming-based bernoulli filter with multiple arrays. Sensors 18(11):4022

    Article  MathSciNet  Google Scholar 

  9. Divya G, Naga, Rao S, Koteswara (2021) Application of sigma point particle filter method for passive state estimation in underwater. Def Sci J 71(4):556–563

    Article  Google Scholar 

  10. Koteswara Rao S, Kavitha Lakshmi M, Jahan, Kausar N, Divya G, Omkar Lakshmi Jagan B (2021) Acceptance criteria of bearings-only passive target tracking solution. IETE J Res. https://doi.org/10.1080/03772063.2021.1906769

  11. Li P, Zhang X, Zhang W (2019) Direction of arrival estimation using two hydrophones: frequency diversity technique for passive sonar. Sensors 19(9):2001

    Article  Google Scholar 

  12. Zhu C, Huang B, Zhou B, Su Y, Zhang E (2021) Adaptive model-parameter-free fault-tolerant trajectory tracking control for autonomous underwater vehicles. ISA Trans 114:57–71

    Article  Google Scholar 

  13. Deng ZC, Yu X, Qin HD, Zhu ZB (2018) Adaptive kalman filter-based single-beacon underwater tracking with unknown effective sound velocity. Sensors 18(12):4339

    Article  Google Scholar 

  14. Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82:35–45

    Article  MathSciNet  Google Scholar 

  15. Fernandez AFG (2011) Detection and tracking of multiple targets using wireless sensor networks. Ph.D. dissertation, Universidad Politecnica de Madrid, Madrid, Spain

  16. Shi W, Song S, Wu C, Chen CLP (2019) Multi pseudo Q-learning-based deterministic policy gradient for tracking control of autonomous underwater vehicles. IEEE Trans Neural Netw Learn Syst 30(12):3534–3546

    Article  MathSciNet  Google Scholar 

  17. Oh R, Shi Y, Choi JW (2021) A hybrid newton-raphson and particle swarm optimization method for target motion analysis by batch processing. Sensors 21(6):2033

    Article  Google Scholar 

  18. Candy JV (2016) Bayesian signal processing: classical, modern, and particle filtering methods, vol 54. Wiley, Hoboken

  19. Simon D (2006) Optimal state estimation: Kalman, H Infinity, and nonlinear approaches. Wiley, Hoboken

  20. Kumar DR, Rao SK, Raju KP (2019) A novel estimation algorithm for torpedo tracking in undersea environment. J Cent South Univ 26(3):673–683

    Article  Google Scholar 

  21. Jahan K, Koteswara Rao S (2019) Extended Kalman filter for bearings-only tracking. Int J Eng Adv Technol 8(6):637–640

    Article  Google Scholar 

  22. Babu Sree Harsha P, Venkata Ratnam D (2018) Fuzzy logic-based adaptive extended Kalman filter algorithm for GNSS receivers. Def Sci J 68(6):560–565

    Article  Google Scholar 

  23. Song T, Speyer J (1985) A stochastic analysis of a modified gain extended Kalman filter with applications to estimation with bearings only measurements. IEEE Trans Autom Control 30(10):940–949

    Article  Google Scholar 

  24. Divya GN, Rao SK (2019) Application and comparison of bayesian framework algorithms for underwater state estimation. In: 2019 International Symposium on Ocean Technology (SYMPOL). IEEE, New York, pp 10-20

  25. Garapati Vaishnavi B, Rao SK, Jahan K (2019) Underwater bearings-only tracking using particle filter. Int J Innov Technol Exploring Eng 8(5):451–455

    Google Scholar 

  26. Einicke GA, White LB (1999) Robust extended Kalman filtering. IEEE Trans Signal Process 47(9):2596–2599

    Article  Google Scholar 

  27. Bar-Shalom Y, Li XR, Kirubarajan T (2004) Estimation with applications to tracking and navigation: theory algorithms and software. Wiley, Hoboken

  28. g Luenberger D (2008) Linear and nonlinear programming. Springer Science+ Business Media, LLC

  29. Jagan BO, Rao SK, Jahan K (2021) Unscented particle filter approach for underwater target tracking. Int J e-Collab (IJeC) 17(4):29–40

    Google Scholar 

  30. Bellaire RL, Kamen EW, Zabin SM (1995) New nonlinear iterated filter with applications to target tracking. Signal and data processing of small targets. Int Soc Opt Photon 2561:240–251

    Google Scholar 

  31. Chaaf A, Muthanna MSA (2021) Energy-efficient relay-based void hole prevention and repair in clustered multi-AUV underwater wireless sensor network. Secur Commun Netw 2021:9969605

    Article  Google Scholar 

  32. Jagan OL, Koteswara Rao S (2020) Underwater surveillance in non-Gaussian noisy environment. Meas Control 53(1–2):250–261

    Article  Google Scholar 

  33. Bell BM, Cathey FW (1993) The iterated Kalman filter update as a Gauss-Newton method. IEEE Trans Autom Control 38(2):294–297

    Article  MathSciNet  Google Scholar 

  34. Moriyama H, Yamashita N, Fukushima M (2003) The incremental Gauss-Newton algorithm with adaptive stepsize rule. Comput Optim Appl 26(2):107–141

    Article  MathSciNet  Google Scholar 

  35. Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 2(2):164–168

    Article  MathSciNet  Google Scholar 

  36. Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431–441

    Article  MathSciNet  Google Scholar 

  37. Skoglund MA, Hendeby G, Axehill D (2015, July) Extended Kalman filter modifications based on an optimization view point. In: 2015 18th International Conference on Information Fusion (Fusion). IEEE, New York, pp 1856-1861

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

    B. Omkar Lakshmi Jagan

  2. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

    S. Koteswara Rao

Authors
  1. B. Omkar Lakshmi Jagan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Koteswara Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B. Omkar Lakshmi Jagan.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jagan, B.O.L., Rao, S.K. Evaluation of DB-IEKF Algorithm Using Optimization Methods for Underwater Passive Target Tracking. Mobile Netw Appl 27, 1070–1080 (2022). https://doi.org/10.1007/s11036-021-01862-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-021-01862-x

Keywords

Search

Navigation