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Stochastic analysis approach of extended H-infinity filter for state estimation in uncertain sea environment

Author

Listed:
  • Guduru Naga Divya

    (Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation)

  • Sanagapallea Koteswara Rao

    (Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation)

Abstract

Underwater surveillance plays a prominent role in civil and military applications which is usually accomplished by bearings-only tracking(BOT) in passive mode. The measurement bearing is nonlinearly related to target state and hence the process of state estimation is nonlinear and these measurements are corrupted with noise which makes process highly nonlinear. Generally, the nonlinear state estimation is done using extended Kalman filter (EKF), unscented Kalman filter (UKF) and so on. However, these filter are able to give solution only when the noise in the measurements is upto 1°. The sea is sometimes so rough that it generates the noise of 8° in the measurements, making the target tracking very challenging. In such a case, the measurements become highly nonlinear and also the noise in the measurements may not be Gaussian making the signal characteristics of noise unknown. Hence the minimax estimator, extended $${{\varvec{H}}}_{\boldsymbol{\infty }}$$ H ∞ filter (EHinfF) which is nonlinear and independent of the signal characteristics of noise present in the measurements is proposed for state estimation. In this research work, the simulation results obtained using Matlab shows that the EHinfF is able to give the solution when the noise in measurements is upto 8° whereas the standard UKF can give solution only when the noise is upto 3°.

Suggested Citation

  • Guduru Naga Divya & Sanagapallea Koteswara Rao, 2024. "Stochastic analysis approach of extended H-infinity filter for state estimation in uncertain sea environment," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(1), pages 152-160, January.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:1:d:10.1007_s13198-022-01682-6
    DOI: 10.1007/s13198-022-01682-6
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