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Modern Radar Detection Theory

This booklet explores those rising study thrusts in radar detection with complex radar platforms in a position to working in difficult eventualities with a plurality of interference assets, either man-made and normal. glossy Radar Detection conception offers a entire reference at the most up-to-date advancements in adaptive radar detection for researchers, complex scholars and engineers engaged on statistical sign processing and its functions to radar platforms.

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Modern Radar Detection Theory

This booklet explores those rising study thrusts in radar detection with complex radar platforms in a position to working in demanding eventualities with a plurality of interference resources, either man-made and ordinary. glossy Radar Detection conception presents a accomplished reference at the most modern advancements in adaptive radar detection for researchers, complex scholars and engineers engaged on statistical sign processing and its functions to radar structures.

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11] F. C. Robey, D. L. Fuhrman, E. J. Kelly, and R. Nitzberg, “A CFAR adaptive matched filter detector,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 29, No. 1, pp. 208–216, January 1992. [12] E. J. Kelly, “An adaptive detection algorithm,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 22, No. 2, pp. 115–127, March 1986. [13] L. L. Scharf, Statistical Signal Processing: Detection, Estimation, and Time Series Analysis, Addison-Wesley, United States, 1991. [14] H. L.

14) and denote by l1 = Re e−jθ p† Za . , e−jθ p† Za ∼ CN (KA, Kσ 2 ). Hence, l1 is a real Gaussian random variable with mean μl1 = KA and variance σl21 = Kσ 2 /2, namely fl1 (t|H1 ) = √ 1 2πσl1 exp − (t − μl1 )2 . 34) where Q(·) is the Q-function [7, p. 6]: 1 Q(x) = √ 2π ∞ exp − x t2 2 dt. , PFA = Q γ Kσ 2 /2 . 6 • Performance Analysis 33 where SNR per pulse is given by SNR = A2 . 24) and denote by g1 = |p† Za|. As already highlighted, under the H1 hypothesis, p† Za ∼ CN (KAejθ , Kσ 2 ). 40) and Iv (x) is the modified Bessel function of the first kind and order v, ∞ Iv (x) = m=0 1 x m!

Shnidman, “Calculation of probability of detection for log-normal target fluctuations,” IEEE Transactions on Aerospace and Electronic Systems, vol. 27, no. 1, pp. 172–174, January 1991. [18] D. A. Shnidman, “Expanded Swerling target models,” IEEE Transactions on Aerospace and Electronic Systems, vol. 39, no. 3, pp. 1059–1069, July 2003. [19] E. Conte and G. Ricci, “Performance prediction in compound-Gaussian clutter,” IEEE Transactions on Aerospace and Electronic Systems, vol. 30, no. 2, pp. 611–616, April 1994.

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