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EC Detection and Estimation Theory | Department of Electronics and Communication Engineering
Write a customer review. Most helpful customer reviews on Amazon. March 9, - Published on Amazon. Verified Purchase. This is a challenging, graduate level book, to be sure. I chose it to study the material independently on recommendation from a colleague who used it as a graduate student at RPI. One of the reasons I settled on it, apart from the recommendation, is that it is less expensive than other texts on this subject at this level. I cannot find a proper solution manual, nor are there any solutions in the back of the book, so independent study has been slow.
I believe this book would be a better resource with the aid of a well-versed professor or TA, but it is a bit dry for self-study. December 12, - Published on Amazon.
E1 244 : Detection and Estimation Theory
I love this book so much, its said almost new but actually it is brand new. However, the final threshold, T , does change because of the extra matched filter gain. The resulting Pfa remains the same compared to the case where only one sample is used to do the detection. However, the extra matched gain improved the Pd from 0. One can run similar cases for the noncoherent receiver to verify the relation among Pd , Pfa and SNR. Radar and sonar applications frequently use pulse integration to further improve the detection performance. If the receiver is coherent, the pulse integration is just adding real parts of the matched filtered pulses.
Thus, the SNR improvement is linear when one uses the coherent receiver. If one integrates 10 pulses, then the SNR is improved 10 times. For a noncoherent receiver, the relationship is not that simple. The following example shows the use of pulse integration with a noncoherent receiver. Assume an integration of 2 pulses. Then, construct the received signal and apply the matched filter to it. One can integrate the pulses using either of two possible approaches.
Both approaches are related to the approximation of the modified Bessel function of the first kind, which is encountered in modeling the likelihood ratio test LRT of the noncoherent detection process using multiple pulses.
The second approach is to sum together abs y from all pulses, which is often referred to as a linear detector. We use square law detector in this simulation. However, the difference between the two kinds of detectors is normally within 0.
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- Example data.
For this example, choose the square law detector, which is more popular than the linear detector. To perform the square law detector, one can use the pulsint function. The function treats each column of the input data matrix as an individual pulse.
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The pulsint function performs the operation of. The relation between the threshold T and the Pfa , given this new sufficient statistics, z , is given by. Using a square law detector, one can calculate the SNR threshold involving the pulse integration using the npwgnthresh function as before. Literatur :  Jerry D. Gibson and James L. Introduction to Nonparametric Detection with Applications.
5 editions of this work
IEEE Press, Signal Detection in Non-Gaussian Noise. Springer Verlag, Prentice Hall, Testing Statistical Hypotheses.
Springer Verlag, 2nd edition, Lehmann and George Casella. Theory of Point Estimation. Probability and Random Processes for Electrical Engineering.
Addison Wesley, 2nd edition, McGraw-Hill, 3rd edition, Vincent Poor.