Representation and generation of non-gaussian wide-sense stationary random processes with arbitrary psds and a class of PDFs

IEEE Transactions on Signal Processing

Volumn 58, Issue 7, 2010, Pages 3448-3458

  Kay, Steven ^a  

^a The University of Rhode Island   (United States)

Author keywords

Radar target recognition; Signal analysis; Signal representations

Indexed keywords

ERGODICITY; FIRST-ORDER; INFINITELY DIVISIBLE; K DISTRIBUTION; NON-GAUSSIAN; PHYSICAL MODELING; RADAR TARGET; SIGNAL REPRESENTATIONS; SINUSOIDAL COMPONENTS; WIDE-SENSE STATIONARIES; WIDE-SENSE STATIONARY RANDOM PROCESS;

MODELS; POWER SPECTRAL DENSITY; RADAR; RADAR TARGET RECOGNITION; RANDOM PROCESSES; RANDOM VARIABLES; SIGNAL ANALYSIS; UNDERWATER ACOUSTICS;

PROBABILITY DENSITY FUNCTION;

EID: 77953758811     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2010.2046437     Document Type: Article

References (21)

1. B. Liu and D. Munson, "Generation of a random sequence having a jointly specified marginal distribution and autocovariance," IEEE Trans. Acoust., Speech, Signal Process., vol.ASSP-30, pp. 973-983, Dec. 1982.
2. E. Conte, M. Lops, and G. Ricci, "Generation of random sequences with prescribed marginal distribution and autocovariance function," presented at the Int. Symp. Commun. Theory Its Appl., Warwick, Scotland, U.K., Sep. 1991.
3. R. S. Raghavan, "A method for estimating parameters of K-distributed clutter," IEEE Trans. Aerosp. Electron. Syst., vol.27, pp. 238-246, Mar. 1991.
4. R. Y. Rubinstein, Simulation and the Monte Carlo Method. New York: Wiley, 1981.
5. E. Jakeman and P. N. Pusey, "A model for non-Rayleigh Sea echo," IEEE Trans. Antennas Propag., vol.AP-24, pp. 806-814, Nov. 1976.
6. M. B. Priestley, Spectral Analysis and Time Series. New York: Academic, 1981.
7. R. Piessens, "The Hankel transform," in Transforms and Applications Handbook, A. Poularikis, Ed. Boca Raton, FL: CRC Press, 2000.
8. T. W. Anderson, The Statistical Analysis of Time Series. New York: Wiley, 1981.
9. D. A. Abraham and A. P. Lyons, "Novel physical interpretation of K-distributed reverberation," IEEE J. Ocean. Eng., vol.27, pp. 800-813, Oct. 2002.
10. I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series, and Products, 5th ed. New York: Academic, 1994, p. 710, 6.565-4.
11. M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions. New York: Dover, 1965.
12. W. Feller, An Introduction to Probability and Its Applications. New York: J. Wiley, 1971, vol.II.
13. S. Primak, V. Lyandres, O. Kaufman, and M. Kliger, "On the generation of a correlated time series with a given probability density function," Signal Process., pp. 61-68, Jan. 1999.
14. J. C. S. Santos Filho and M. D. Yacoub, "Coloring non-Gaussian sequences," IEEE Trans. Signal Process., vol.56, pp. 5817-5822, Dec. 2008.
15. D. Middleton, "Non-Gaussian noise models in signal processing for telecommunications: New methods and results for Class A and Class B noise models," IEEE Trans. Inf. Theory, vol.45, pp. 1129-1149, May 1999.
16. S. Kay, Modern Spectral Estimation: Theory and Application. Englewood Cliffs, NJ: Prentice-Hall, 1988.
17. S. Kay, Intuitive Probability and Random Processes Using MATLAB. New York: Springer, 2006.
18. N. Levanon and E. Mozeson, Radar Signals. New York:Wiley, 2004.
19. L. Jackson, S. Kay, and N. Vankalayapati, "An iterative method for nonlinear FM synthesis of radar signals," IEEE Trans. Aerosp. Electron., vol.46, pp. 910-917, May 2010.
20. L. Bondesson, "On simulation from infinitely divisible distributions," Adv. Appl. Prob., pp. 855-869, 1982.
21. P. J. Brockwell and R. A. Davis, Time Series: Theory and Methods. New York: Springer-Verlag, 1987, p. 334.