Huang Yong, Peng Yingning . DESIGN OF RECURSIVE MTI FILTER BASED ON THE GENETIC ALGORITHM[J]. Journal of Electronics & Information Technology, 2000, 22(6): 1001-1006.
Citation:
Huang Yong, Peng Yingning . DESIGN OF RECURSIVE MTI FILTER BASED ON THE GENETIC ALGORITHM[J]. Journal of Electronics & Information Technology, 2000, 22(6): 1001-1006.
Huang Yong, Peng Yingning . DESIGN OF RECURSIVE MTI FILTER BASED ON THE GENETIC ALGORITHM[J]. Journal of Electronics & Information Technology, 2000, 22(6): 1001-1006.
Citation:
Huang Yong, Peng Yingning . DESIGN OF RECURSIVE MTI FILTER BASED ON THE GENETIC ALGORITHM[J]. Journal of Electronics & Information Technology, 2000, 22(6): 1001-1006.
In theory, it is possible to synthesize any frequency response curve with the recursive MTI(Moving Target Indication) filters. As a result, the applied field of this kind of filter is wide. A new design method is presented to design flexibly a recursive MTI filter that can meet the specifications of the system. The method is based on the Genetic Algorithm(GA) and constitutes the fitness function with improvement factor, passband width and ripple of a filter. It speeds the search process and achieves near-global optimum parameters by means of the GA. Two kinds of classical recursive MTI filters are designed in this paper. The results demonstrate the validity of this design method that can design flexibly and quickly a required filter.
Galati G (Ed.). Advanced Radar Techniques and Systems. London, UK: Peter Peregrinus Ltd.,1993, Chapter 6.[2]Schleher D C. MTI and Pulsed Doppler Radar. Norwood, MA: Artech House, Inc., 1991, 361-384.[3]Skolnik M I. Radar Handbook. New York: McGraw-Hill, 1990, 15.23-15.28.[4]Mark J W, Woods H A. A recursive digital MTI radar filter. Proc. IEEE, 1972, 60(6): 728-729.[4]Mark J W, Woods H A. Improvement factor of a recursive MTI radar filter[J].Proc. IEEE.1972,60(11):1442-1443[5]Klemm R. Multiplier-free filters for ground clutter suppression. IEE Proc.-F, 1986, 133(1): 12-15.[6]Hassoun M H. Fundamentals of Artificial Neural Networks. Cambridge, MA.: MIT Press, 1995,439-452.[7]Tang K S, Man K F, Kwong S, et al. Genetic algorithms and their applications[J].IEEE Signal Processing Magazine.1996, 13(6):22-37