Adaptive detection using low rank approximation to a data matrix

IEEE Transactions on Aerospace and Electronic Systems

Volumn 30, Issue 1, 1994, Pages 55-67

  Kirsteins, Ivars P ^a   Tufts, Donald W ^b  

^a Naval Undersea Warfare Center Detachement New London   (Germany)

^b UNIVERSITY OF RHODE ISLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; DATA PROCESSING; ELECTRIC FILTERS; ERROR ANALYSIS; INTERFERENCE SUPPRESSION; INVERSE PROBLEMS; MATRIX ALGEBRA; PERFORMANCE; SIGNAL FILTERING AND PREDICTION; STATISTICS; VECTORS; WAVEFORM ANALYSIS;

ACTIVE SONAR REVERBERATION DATA; ADAPTIVE DETECTION; DATA MATRIX; GENERALIZED LIKELIHOOD RATIO TEST; INTERFERENCE CANCELLATION; INTERFERENCE WAVEFORM; LOW RANK APPROXIMATION; PRINCIPAL COMPONENT INVERSE; RESIDUAL WAVEFORM;

SIGNAL DETECTION;

EID: 0028196115     PISSN: 00189251     EISSN: None     Source Type: Journal    
DOI: 10.1109/7.250406     Document Type: Article

References (37)

1. Scharf, L. I,., and Tufts, D. W. (1987) Rank reduction for modelling stationary signals. IEEE Transactions on Acoustics, Speech, Signal Processing, ASSP-35 (Mar 1987), 350–355.
2. Scharf, L. L. (1984) The SVD and reduced rank signal processing. Technical report, Dept, of Electrical and Computer Engineering, University of Colorado, Boulder, Oct. 1984.
3. Behrens, R. T. (1990) Subspace signal processing in structured noise. Ph.D. dissertation, University of Colorado, Boulder, 1990.
4. Tufts, D. W., Kirsteins, I. P., and Kumaresan, R. (1984) Data-adaptive detection of a weak, single-frequency, plane-wave signal in noise and strong, unidirectional interference. In E. J. Wegman and J. G. Smith (Eds.), Statistical Signal Processing. New York: Marcel Deckker, 1984, 117–131.
5. Tufts, D. W., Kirsteins, I. P., and Kumaresan, R. (1983) Data adaptive detection of a weak signal. IEEE Transactions on Aerospace and Electronic Systems, AES-19 (Mar. 1983), 313–316.
6. Kirsteins, I. P, and Tufts, D. W. (1984) On the probability density of signal-to-noise ratio in an improved adaptive detector. Technical report, Office of Naval Research, 1984.
7. Kirsteins, I. P., and TUfts, D. W. (1985) On the probability density of signal-to-noise ratio in an improved detector. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Tampa, FL, Mar. 1985, 572–575.
8. Kirsteins, I. P., and Tufts, D. W. (1989) Rapidly adaptive nulling of interference. In Proceedings of GRETS I Conference, Juan-Les-Pins, France, June 1989.
9. Kirsteins, I. P., and Tufts, D. W. (1989) Rapidly adaptive nulling of interference. In Proceedings of the International Conference on System Engineering, Dayton, OH, Aug. 1989, 269–272.
10. Kirsteins, I. P., and Tufts, D. W. (1991) Rapidly adaptive nulling of interference. In M. Bouvet and G. Bienvenu (Eds.), Lecture Notes in Control and Information Sciences. New York: Springer-Verlag, 1991.
11. Kumaresan, R., and Verma, S. (1986) On estimating the parameters of chirp signals using rank reduction techniques. In Proceeding of Nineteenth Annual Asilomar Conference on Signals, Systems and Computers, 1986, 555–558.
12. Applebaum, S. P. (1976) Adaptive arrays. IEEE Transaction on Antennas Propagation, AP-24 (Sept. 1976), 585–598.
13. Brennan, L. E., and Reed, I. S. (1973) Theory of adaptive radar. IEEE Transactions on Aerospace and Electronic Systems, AES-9 (Mar. 1973), 237–251.
14. Reed, I. S., Mallet, J. D., and Brennan, L. E. (1974) Rapid convergence rate in adaptive arrays. IEEE Transactions on Aerospace and Electronic Systems, AES-10 (Nov. 1974), 885–863.
15. Kelly, E. J. (1986) An adaptive detection algorithm. IEEE Transactions on Aerospace and Electronic Systems, AES-23 (Nov. 1986), 115–127.
16. Tufts, D., Kumaresan, R., and Kirsteins, I. (1982) Data adaptive signal estimation by singular value decomposition of a data matrix. Proceeding of the IEEE, 7 (1982), 684–685.
17. Shah, A. A, and Tufts, D. W. (1992) Estimation of the signal component of a data vector. In Proceeding of the International Conference on Acoustics, Speech, and Signal Processing, San Francisco, CA, Mar. 1992, 393–396.
18. Golub, G., and Loan, C. V. (1984) Matrix Computations. Baltimore, MD: Johns Hopkins University Press, 1984.
19. Eckart, C., and Young, G. (1936) The approximation of one matrix by another of lower rank. Psychometrica, 1 (1936), 211–218.
20. Claus, A. J., Kadota, T. T., and Romain, D. M. (1980) Efficient approximation of a family of noises for application in adaptive spatial processing for signal detection. IEEE Transactions on Information Theory, IT-26 (Sept. 1980), 588–595.
21. Liu, S. C., and Nolte L. W. (1980) Performance evaluation of array processors for detecting Gaussian signals. IEEE Transactions on Acoustics, Speech, Signal Processing, ASSP-28 (June 1980), 328–333.
22. Kirsteins, I. P (1985) A reduced-rank generalized likelihood-ratio test. In Proceeding of NATO AS I Conference on Acoustics and Signal Processing for Ocean Exploration, Madeira, Portugal, July 1985, 572–575.
23. Tufts D. W, Kil, D. H., and Slater, R. R. (1992) Reverberation suppression and modeling. In Dale Ellis, John Preston, Heinz Urban (Editors), Ocean Reverberation Kluwer Academic Press, Dordrecht, The Netherlands, 1993.
24. Vaccaro, R. J., Tufts, D. W., and Boudreaux-Bartels, G. F. (1988) Advances in principal component signal processing. In E. Deprettere (Ed.), SVD and Signal Processing. Amsterdam: Elsevier Science Publishers B.V., 1988, 115–146.
25. Porat, B., and Friedlander, B. (1987) An accuracy analysis of the Kumaresan-TUfts for estimating complex damped exponentials. IEEE Transactions on Acoustics, Speech, Signal Processing, ASSP-35 (Feb. 1987), 231–235.
26. Tufts, D. (1990) The effects of perturbations on matrix-based signal processing. In Proceeding of Fifth ASSP Workshop on Spectrum Estimation and Modeling, 1990, 159 162
27. Tufts, D. W., Vaccaro, R. J., and Kot, A. C. (1989) Analysis of estimation of signal parameter by linear-prediction at high SNR using matrix approximation. In Proceeding of the International Conference on Acoustics, Speech, Signal Processing, May 1989, 2194–2197.
28. Li, F„ Vaccaro, R. J., and Tufts, D. W. (1989) Min-norm linear prediction for arbitrary sensor array. In Proceeding of the International Conference on Acoustics, Speech, Signal Processing, Glasgow, UK, May 1989, 2613–2616.
29. Li, F., Vaccaro, R. J., and Tufts, D. W. (1990) Unified performance analysis of subspace-based estimation algorithms. In Proceeding of the International Conference on Acoustics, Speech, Signal Processing, Albuquerque, NM, Apr. 1990, 2575–2578.
30. Li, F., and Vaccaro, R. J. (1989) MUSIC performance prediction by matrix approximation at high SNR. In Proceeding of the 1989 Conference on Information Sciences and Systems, The Johns Hopkins University, Baltimore, MD, Mar. 1989, 447–481.
31. Li, F., and Vaccaro, R. J. (1990) Analysis of MUSIC and Min-Norm for arbitrary array geometry. IEEE Transactions on Aerospace and Electronic Systems, 26 (Nov. 1990), 976–985.
32. Li, F (1990) A unified performance analysis of subspace-based DOA estimation algorithms. Ph.D. dissertation, University of Rhode Island, Kingston, 1990.
33. Kirsteins, I. P (1990) Analysis of reduced rank interference cancellation. Ph.D. dissertation, University of Rhode Island, Kingston, 1990.
34. Tufts, D. W, Kot. A C, and Vaccaro, R. J. (1988) The threshold analysis of SVD-based algorithms. In Proceedings of the International Conference on Acoustics, Speech, Signal Processing, Apr. 1988, 2416- 2419.
35. Tufts, D. W., Kot, A. C. and Vaccaro, R. J. 1991 The threshold effect in signal processing algorithms which use an estimated subspace. In R. J. Vaccaro (Ed.), SVD and Signal Processing II. Amsterdam: Elsevier Science Publishers, 1991.
36. Poor, H. (1988) An Introduction to Signal Detection and Estimation. New York: Springer-Verlag, 1988.
37. Styan, G, P. H. (1989) Three useful expressions for expectations involving a wishart matrix and its inverse. In Statistical Data Analysis and Inference. Amsterdam: Elsevier Science Publishers, B. V, 1989.