Improved relocatable over-the-horizon radar detection and tracking using the maximum likelihood adaptive neural system algorithm

Radio Science

Volumn 33, Issue 4, 1998, Pages 1113-1124

  Perlovsky, Leonid I ^a   Webb, Virgil H ^a   Bradley, Scott R ^a   Hansen, Christopher A ^b  

^a Nichols Research Corporation ^*  (United States)

^b AIR FORCE RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AIRCRAFT DETECTION; ALGORITHMS; CLUTTER (INFORMATION THEORY); FIGHTER AIRCRAFT; FREQUENCY ESTIMATION; MAXIMUM ENTROPY METHODS; MAXIMUM LIKELIHOOD; MAXIMUM LIKELIHOOD ESTIMATION; PROBABILITY DENSITY FUNCTION; RADAR; RADAR CLUTTER; RADAR SYSTEMS; SIGNAL TO NOISE RATIO; SPECTRUM ANALYSIS; TARGET TRACKING; TRACKING (POSITION); TRACKING RADAR; ADAPTIVE ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS; PROBABILITY;

DETECTION AND TRACKING; ENGINEERING METHODOLOGY; EXPECTED IMPROVEMENTS; MODEL BASED NEURAL NETWORKS; OVER-THE-HORIZON RADAR; PROBABILITY DENSITIES; SPECTRUM ESTIMATION TECHNIQUES; THEORETICAL FOUNDATIONS;

RADAR TRACKING;

MAXIMUM LIKELIHOOD ADAPTIVE NEURAL SYSTEMS (MLANS); RELOCATABLE OVER THE HORIZON RADAR (ROTHR);

EID: 0032117003     PISSN: 00486604     EISSN: None     Source Type: Journal    
DOI: 10.1029/98RS00445     Document Type: Article

References (16)

1. Dempster, A.P., N.M. Laird, and D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc., 39B, 1-38, 1977.
2. Einstein, A., and L. Hopf, Uber einen Satz der Wahrscheinlichkeit-srechnung und seine Anwendung an die Strahlungstheorie, Ann. Phys., 33, 1096-1104, 1910.
3. Headrick, J.M., Looking over the horizon, IEEE Spectrum, 36-39, July 1990.
4. Kay, S.M., and S.L. Marple, Jr., Spectrum analysis - A modern perspective, Proc. IEEE, 69(11), 1380-1419, 1981.
5. Perlovsky, L.I., Multiple sensor fusion and neural networks, DARPA neural network study, Lincoln Lab., Mass. Inst. of Technol., Lexington, 1987.
6. Perlovsky, L.I., Computational concepts in classification: Neural networks, statistical pattern recognition, and model based vision, J. Math. Imaging Vision, 4(1), 81-110, 1994a.
7. Perlovsky, L.I., A model based neural network for transient signal processing, Neural Networks, 7(3), 565-572, 1994b.
8. Perlovsky, L.I., Einstein, likelihood, and information, paper presented at the Conference on Intelligent Systems and Semiotics '97, IEEE, Gaithersburg, Md., Sept 22-25, 1997a.
9. Perlovsky, L.I., Cramer-Rao bound for tracking in clutter and tracking multiple objects, Pattern Recognition Lett., 18(3), 283-288, 1997b.
10. Perlovsky, L.I. and M.M. McManus, Maximum likelihood neural networks for sensor fusion and adaptive classification, Neural Networks, 4(1), 89-102, 1991.
11. Perlovsky, L.I., W.H. Schoendorf, D.M. Tye, and W. Chang, Concurrent classification and tracking using maximum likelihood adaptive neural system, J. Underwater Acoust., 45(2), 399-414, 1995a.
12. Perlovsky, L.I., C.P. Plum, P.R. Franchi, E.J. Tichovolsky, D.S. Choi, and B. Weijers, Estimation of ionospheric effects on radar spectra, paper presented at the International Conference on Signal Processing Applications and Technology, IEEE, Boston, Mass., Nov. 22-24, 1995b.
13. Perlovsky, L.I., W.H. Schoendorf, B.J. Burdick, and D.M. Tye, Model-based neural network for target detection in SAR images, IEEE Trans. Image Process., 6(1), 203-216, 1997a.
14. Perlovsky, L.I., C.P. Plum, P.R. Franchi, E.J. Tichovolsky, D.S. Choi, and B. Weijers, Einsteinian neural network and spectrum estimation, Neural Networks, 10(9), 1541-1546, 1997b.
15. Perlovsky, L.I., W.H. Schoendorf, L.C. Garvin, and W. Chang, Development of concurrent classification and tracking, J. Underwater Acoust., in press, 1997c.
16. Pillai, S. U. and T.I. Shim, Spectrum Estimation and System Identification, Springer Verlag, New York, 1993.