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Target Detection in Radar: Current Status and Future Possibilities

Published online by Cambridge University Press:  23 November 2009

Vincent Y. F. Li and
Keith M. Miller
Vincent Y. F. Li
Affiliation:
(Hong Kong University of Science and Technology)
Keith M. Miller
Affiliation:
(Institute of Marine Studies, University of Plymouth)
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Extract

Most of the radar systems used in operating marine vessel traffic management services experience problems, such as track loss and track swap, which may cause confusion to the traffic regulators and lead to potential hazards in the harbour operation. The reason is mainly due to the limited adaptive capabilities of the algorithms used in the detection process. The decision on whether a target is present is usually based on the amplitude information of the returning echoes. Such method has a low efficiency in discriminating between the target and clutter, especially when the signal-to-noise ratio is low. With modern signal processing techniques more information can be extracted from the radar return signals and the tracking parameters of the previous scan. The objectives of this paper are to review the methods which are currently adopted in radar target identification, identify techniques for extracting additional information and consider means of data analysis for deciding the presence of a target. Instead of employing traditional two-state logic, it is suggested that the radar signal should be allocated in terms of threshold levels into fuzzy sets with its membership functions being related to the information extracted and the environment. Additional signal processing techniques are also suggested to explore pattern recognition aspects and discriminate features which are associated with a return signal from those of clutter.

Keywords

1. Radar.2. Signal processing.3. Target detection.

Information

Type
Research Article
Information
The Journal of Navigation , Volume 50 , Issue 2 , May 1997, pp. 303 - 313
Copyright
Copyright © The Royal Institute of Navigation 1997

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References

REFERENCES

1Barkat, M., (1996). CFAR detection for multiple target situations, IEE Proceedings, vol. 136, no. 5, October, 193–209.Google Scholar
2Steenson, B. O., (1968). Detection performance of a mean-level threshold, IEEE transactions on Aerospace and Electronic Systems, AES-4, July, 529–534.CrossRefGoogle Scholar
3Kassam, S. A., (1988). Analysis of CFAR Processors in Nonhomogeneous background, IEEE Transactions on Aerospace and Electronic Systems, vol. 24, July, 427–444.Google Scholar
4Hansen, V. G., and Sawyers, X., (1980). Detectability loss due to greatest of selection in a cell averaging CFAR, IEEE transactions on Aerospace and Electronic Systems, AES-16, 115–118.CrossRefGoogle Scholar
5Al-Hussaini, . (1988). Performance of the greater-of and censored greater-of detectors in multiple target environments, IEE proceedings, vol. 135, June, 193–198.Google Scholar
6Weiss, M., (1982). Analysis of some modified cell-averaging CFAR processors in multipletarget situations, IEEE Transactions on Aerospace and Electronic Systems, AES-18, January, 102–103.CrossRefGoogle Scholar
7Trunk, G. V., (1983). Range resolution of targets using automatic detectors, IEEE Transactions on Aerospace and Electronic Systems, AES-19, July, 750–755.Google Scholar
8Rohling, H., (1983). Radar CFAR thresholding in clutter and multiple target situations, IEEE Transactions on Aerospace and Electronics System, AES-19, 608–612.CrossRefGoogle Scholar
9Bovik, A. C., Huang, T. S., and Munson, D. C. Jr. (1983). A generalization of median filtering using linear combinations of order statistics, IEEE Transactions on Acoustic, Speech and Signal Processing, ASSP-31, 1342–1350.CrossRefGoogle Scholar
10Wilson, S. I., (1993). Two CFAR algorithms for interfering targets and nonhomogeneous clutter, IEEE Transactions on Aerospace and Electronic Systems, vol. 29, 57–71.CrossRefGoogle Scholar
11Rickard, J. T., and Dillard, G. M., (1971). Adaptive detection algorithms for multiple target situations, EEE Trans., AES-13 (4), 338–343.Google Scholar
12Ritcey, J. A., (1986). Performance analysis of the censored mean level detector, IEEE Trans, AES-22, 443–454.Google Scholar
13Al-Hussaini, E. K., (1988). Performance of the greater-of and censored greater-of detectors in multiple target environments, IEE Proceedings, vol. 135, 193–198.Google Scholar
14Vassilis Anastassopoulos and Lampropoulos, G. A., (1992). A new and robust CFAR detection algorithm, IEEE Transactions on aerospace and electronic systems, vol. 28, 420–427.Google Scholar
15Jain, A. K., (1989). Fundamentals of Digital Image Processing, Prentice-Hall International Editions.Google Scholar
16Stevenson, R. L., and Arce, G. R., (1987). Morphological filters: statistics and further syntactic properties, IEEE Transactions on Circuits and System, November, 1292–1305.CrossRefGoogle Scholar
17Kosko, B., (1992). Neural networks and fuzzy systems, Prentice-Hall, Inc.Google Scholar
18Russo, F., and Ramponi, G., (1992). Fuzzy operator for sharpening of noisy images, IEE Electronics Letters, August, 1571–1717.Google Scholar
19Russo, F., (1992). Afuzzy approach to digital signal processing: concepts and applications, IEEE, 640–645.Google Scholar
20Li, Y. F., and Lau, C. C., (1989). Development of fuzzy algorithms for servo systems. IEEE Control Systems Magazine, April, 65–71.CrossRefGoogle Scholar
21Boston, J. R., (1993). A fuzzy model of signal detection incorporating uncertainty, IEEE, 1107–1112.Google Scholar
22Russo, F., (1994). A totally fuzzy approach to multisensor instrumentation: pre-processing techniques, IEEE, 1143–1146.Google Scholar
23Son, J. C., Song, I., and Kim, S., (1991). Afuzzy set theoretic approach to signal detection, IEEE, 150–153.Google Scholar
24Van Trees, H. L., (1968). Detection, Estimation, and Modulation Theory, Part 1, John Wiley and Sons, New York.Google Scholar
25Zadeh, L. A., (1965). Fuzzy sets, Inform, and Control 8, 338–353.Google Scholar
26Saade, J. J., (1994). Towards intelligent radar systems, Fuzzy Sets and Systems 63, 141–157.CrossRefGoogle Scholar
27Saade, J. J., (1992). Ordering fuzzy sets over the real line: An approach based on decision making under uncertainty, Fuzzy Sets and Systems 50, 237–246.CrossRefGoogle Scholar
28Saade, J. J., (1990). Fuzzy hypothesis testing with hybrid data, Fuzzy Sets and Systems 35, 197–212.CrossRefGoogle Scholar
29Benelli, G., Garzelli, A., and Mecocci, A., (1994). Complete processing system that uses fuzzy logic for ship detection in SAR images, IEE Pro.-Radar, Sonar Navig., vol. 141, 181–186.CrossRefGoogle Scholar
30Cho, S. M., and Cho, J. H., (1994). Thresholding for edge detection using fuzzy reasoning technique, Singapore ICCS, 1121–1124.Google Scholar
31Guirong, , Guo, (1989). An intelligence recognition method of ship targets, IEEE, 1088–1096.Google Scholar
32Guirong, , Guo, (1992). Target detection and recognition based on dynamic processing techniques, IEEE 171–181.Google Scholar
33Baldygo, W., (1993). Artificial intelligence applications to constant false alarm rate processing, IEEE 275–280.Google Scholar
34Kaveh, M., and Barabell, A. J., (1986). The statistical performance of the MUSIC and the minimum-norm algorithms in resolving plane waves in noise, IEEE Trans. Acoust. Speech. Signal Processing, vol. ASSP-34, 331–341.CrossRefGoogle Scholar
35Roy, R., and Kailath, T., (1989). ESPRIT – estimation of signal parameters via rotation invariance techniques, IEEE Trans. Acoust., Speech, Signal processing, vol. ASSP-37, 984–995.CrossRefGoogle Scholar
36Wax, M., and Kailth, T., (1985). Detection of signals by information theoretic criteria, IEEE Tran. Acoust. Speech. Signal Processing, vol. ASSP-33, 387–392.CrossRefGoogle Scholar