Poisson representation and Monte Carlo estimation of generalized Marcum Q-function

IEEE Transactions on Aerospace and Electronic Systems

Volumn 42, Issue 4, 2006, Pages 1520-1531

  Weinberg, Graham V ^a  

^a DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ESTIMATION; GAUSSIAN DISTRIBUTION; MONTE CARLO METHODS; POISSON DISTRIBUTION; PROBABILITY; RANDOM PROCESSES;

GENERALIZED MARCUM Q-FUNCTION; MONTE CARLO ESTIMATION; POISSON RANDOM VARIABLES; POISSON REPRESENTATION;

AEROSPACE ENGINEERING;

EID: 33947644810     PISSN: 00189251     EISSN: None     Source Type: Journal    
DOI: 10.1109/TAES.2006.314593     Document Type: Article

References (19)

1. Marcum, J. L. A statistical theory of target detection by pulsed radar. IRE Transactions on Information Theory, IT-6 (1960), 59-144.
2. Marcum, J.I. Table of Q functions. Rand Corp., Santa Monica, CA, 1950, U.S.A.E. Project RAND Research Memorandum M-339.
3. Marcum, J. I., and Swerling, P. Studies of target detection by pulsed radar. IEEE Transactions on Information Theory, IT-6 (1960), 59-308.
4. Helstrom, C. W. Computing the generalized Marcum Q-function. IEEE Transactions on Information Theory, 38 (1992), 1422-1428.
5. M function. IEEE Transactions on Information Theory, 21 (1975), 95-96.
6. Shnidman, D. A. The calculation of the probability of detection and the generalised Marcum Q-function. IEEE Transactions on Information Theory, 35 (1989), 389-400.
7. Simon, M. K. A new twist on the Marcum Q-function and its application. IEEE Communications Letters, 2 (1998), 39-41.
8. Simon, M. K., and Alouini, M-S. Some new results for integrals involving the generalised Marcum Q function and their application to performance evaluation over fading channels. IEEE Transactions on Wireless Communications, 2 (2003), 611-615.
9. Lyness, J. N., and Kaganove, J. J. Comments on the "Nature of Automatic Quadrature Routines." ACM Transactions on Mathematical Software, 2 (1976), 65-81.
10. Bowman, F. Introduction to Bessel Functions. New York: Dover, 1958.
11. Helstrom, C. W. Statistical Theory of Detection. New York: Pergamon, 1968.
12. Tsypkin, A. G., and Tsypkin, G. G. Mathematical Formulas: Algebra, Geometry and Mathematical Analysis. Moscow: Mir Publishers, 1988.
13. Billingsley, P. Probability and Measure. New York: Wiley, 1995.
14. Robert, C. P., and Casella, G. Monte Carlo Statistical Methods. New York: Springer, 2004.
15. Ross, S. M. Simulation. San Diego, CA: Academic Press, 2002.
16. Srinivasan, R. Importance Sampling: Applications in Communications and Detection. Heidelberg: Springer-Verlag, 2002.
17. Srinivasan, R. Simulation of CFAR detection algorithms for arbitrary clutter distributions. IEE Proceedings Radar Sonar Navigation, 147 (2000), 31-40.
18. Bucklew, J. A., and Gubner, J. A. Bias point selection in the importance sampling Monte Carlo simulation of systems. IEEE Transactions on Signal Processing, 51 (2003), 152-159.
19. Durrett, R. Probability: Theory and Examples. Belmont, CA: Wadsworth Publishing Company, 1996.