AIRCRAFT TARGET RECOGNITION BASED ON RADAR RANGE PROFILE SEQUENCES USING RECURRENT NEURAL NETWORK

Fielding K, Ruck D. Spatio-temporal pattern recognition using hidden Markov models. IEEE Trans. on AES, 1994, AES-31(4): 1292-1300.[2]Farhat N H. Microwave diversity imaging and automated target identification based on models[3]of neural networks. Proc[J].IEEE.1989, 77(5):670-680[4]Jouny I, Garber F. Classificaton of radar targets using synthetic neural networks. IEEE Trans.[5]on AES, 1993, AES-29(2): 336-344.[6]Botha E C,et al. Feature based classification of aerospace radar targets using neural networks[J].Neural Networks.1996, 9(1):129-142[7]Hudson S, Psaltis D. Correlation filters for aircraft identification from radar range profiles. IEEE[8]Trans. on AES, 1993, AES-29(3): 741-748.[9]Zyweck A, Bogner R E. Radar target classification of commercial aircraft. IEEE Trans. on AES, 1996, AES-32(2): 598-606.[10]Vermeulen P,et ad. Radar target recognition with neural nets. The Trans. of the South African[11]Institute of Electrical Engineers, 1993, 84(2): 174-180.[12]Tank D W, Hopfield J. Neural computation by concentrating information in time. Proceedings[13]of the National Academy of Science of the USA, 1987, 84: 1896-1900.[14]Seidl D R, Lorenz R D. A structure by which a recurrent neural network can approximate a[15]nonlinear dynamic system. Proceedings of the JCNN, Seattle: 1991, 709-714.[16]Lindsey, Randall L. Fuction prediction using recurrent neural networks: [MS thesis]. AFIT/GE/ENG/91D-02.[17]Williams R J, Zipser D. An algorithm for continually running fully recurrent neural networks[J].Neural computation.1989, 1:270-280