Measurements Preprocessing for Bearing and Frequency Target Motion Analysis

BFTMA를 위한 측정데이터 전처리 기법 연구

  • Published : 2004.06.01

Abstract

In this paper, the measurements preprocessing algorithm for the fading of bearing and frequency measurements is proposed, which can improve the performance of BFTMA(Bearing and Frequency Target Motion Analysis). The fading and detection relation between bearing and frequency are rigorously established for measurements preprocessing, and BFTMA can be carried out the estimation of target motion by using measurements preprocessing. Batch estimation with bearing and frequency using the proposed algorithm can be applied to estimate the initial target states despite of the fading of frequency measurement. Simulation results show that BFTMA using the proposed measurements preprocessing has superior estimation performance, compared with batch estimation using only bearing measurements.

Keywords

References

  1. L. Gerken, ASW versus Submarine Technology Battle, American Scientific Corp, 1986
  2. D. H. Wagner, W. C. Mylander and T. J. Sanders, Naval Operations Analysis, Naval Institute Press, 1999
  3. C. Jauffret and D. PilIon, 'Observability in Passive Target Motion Analysis,' IEEE Transactions on Aerospace and Electronic Systems, Vol. 32, No. 4, pp.1290-1300, October, 1996 https://doi.org/10.1109/7.543850
  4. C. Jauffret and D. Pilion, 'New Observability Criterion in Target Motion Analysis,' Underwater Acoustic Data Processing, pp. 479-484, 1989
  5. A. G. Lindgren and K. F. Gong, 'Position and Velocity Estimation via Bearing Observations,' IEEE Transactions on Aerospace and Electronic Systems, Vol. 14, pp.564-577, July, 1978
  6. V. J. Aidala, 'Kalman Filter Behavior in Bearings-Only Tracking Applications,' IEEE Transactions on Aerospace and Electronic Systems, Vol. 15, No. 2, pp.29-39, January, 1979
  7. S. C. Nardone and V. J. Aidala, 'Observability Criteria For Bearings-Only Target Motion Analysis,' IEEE Transactions on Aerospace and Electronic Systems, Vol. 17, No.2, pp.162-166, March, 1981 https://doi.org/10.1109/TAES.1981.309141
  8. V. J. Aidala and S. C. Nardone, 'Biased Estimation Properties of the Pseudolinear Tracking Filter,' IEEE Transactions on Aerospace and Electronic Systems, Vol. 18, No. 4, pp.432-441, July, 1982 https://doi.org/10.1109/TAES.1982.309250
  9. T. L. Song and J. L. Speyer, 'A Stochastic Analysis of a Modified Gain Extended Kalman Filter with Applications to Estimation with Bearings Only Measurements,' IEEE Transactions on Automatic Control, Vol. 30, No. 10, pp.940-949, October, 1985 https://doi.org/10.1109/TAC.1985.1103821
  10. Y. Bar-Shalom and X. R. Li, Estimation and Tracking Principles, Techniques, and Software, Artech House, 1993
  11. P. S. Maybeck, Stochastic Models, Estimation, and Control, Vol. 1, Academic Press, 1979
  12. M. J. Maron, Numerical Analysis, A Practical Approach, Macmillan Publishing, 1982