The Applications of Neural Network Filter to Estimation of Oil Spoil in Sea

Abstract:

The oil spoil detection is an important problem in many applications such as oil exploration and transportation. But in some application such as sea, this task is very difficult because of the strong clutter. Many algorithms have been proposed for this problem. The Maximum Likelihood (ML) is one of those good solutions. This paper describes an application of neural network (NN) for obtaining the global optimal solution of finding oil spoil. It improves the estimation accuracy. The computation complexity is modest.

• Periodical: Advanced Materials Research (Volumes 889-890)
• Pages: 1625-1629
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.889-890.1625
• Online since: February 2014
• Authors: Yu Ming Tong, Di Jian Xu, Jin Liang Shi, Jian Kui Zeng*
• Keywords: Artificial Neural Network (ANN), Maximum Likelihood, Oil Spoil, Parameter Estimation, Target Detection

References

[1] F. C. Schweppe, Sensor array data processing for multiple signal sources, IEEE Trans. On Inform. Theory Vol. 14, No. 3, pp.294-305, (1968).

DOI: 10.1109/tit.1968.1054121

[2] R. O. Schmidt, Multiple emitter location and signal parameter estimation, IEEE Trans., on Antennas Propagat., Vol. 34, No. 3, pp.276-280, (1986).

DOI: 10.1109/tap.1986.1143830

[3] S. S. Reddi, Multiple source location- A digital approach, IEEE Trans. Aerosp. Electron. Syst., vol. AES-15, pp.95-105, (1979).

DOI: 10.1109/taes.1979.308800

[4] R. Kumaresan and D. W. Tufts, Estimating the angles of arrival of multiple plane waves, IEEE Trans. Aerosp. Electron. Syst., vol. AES-19, pp.134-139, (1983).

DOI: 10.1109/taes.1983.309427

[5] L. C. Godara, application of Antenna Array to mobile Communications, Part II: Beam-Forming and Direction-of-Arrivial Considerations, Proc. IEEE, vol. 85, pp.1195-1245, (1997).

DOI: 10.1109/5.622504

[6] I. Ziskind and M. Wax, Maximum likelihood localization of multiple sources by alternating projection, IEEE Trans. on Acoust., Speech, Signal Processing, Vol. 36, No. 10, pp.1553-1560, (1988).

DOI: 10.1109/29.7543

[7] K. Sharman, Maximum Likelihood estimation by simulated annealing", Proc., ICASSP, 88, New York, pp.2741-2744, (1988).

[8] P. Stoica and A. B. Gershaman, Maximum-Likelihood DOA Estimation by Data-Supported Grid Search, IEEE Signal Processing Letters, Vol. 6, pp.273-275, (1999).

DOI: 10.1109/97.789608

[9] P. Stoica and K. C. Sharman, Maximum Likelihood Methods for Direction-of-Arrivial estimation, IEEE Trans. Acoust., Speech, Signal Processing, Vol. 38, pp.1132-1143, (1990).

DOI: 10.1109/29.57542

[10] Shi Y, Eberhart R. A modified particle optimizer. Proceedings of the IEEE international coference on evolutionary computation.  Piscataway, NJ, IEEE Press; pp.69-73, (1998).

[11] Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of the IEEE international conference on neural networks (Perth, Australia), 1942-1948. Piscataway, NJ: IEEE Service Center; (1995).