Signal Detection Theory by Vyacheslav P. Tuzlukov (auth.)
By Vyacheslav P. Tuzlukov (auth.)
Increasing the noise immunity of advanced sign processing platforms is the most challenge in numerous parts of sign processing. this day there are lots of books and periodical articles dedicated to sign detection, yet many very important difficulties stay to be solved. New methods to advanced difficulties permit us not just to summarize investigations, but in addition to enhance the standard of sign detection in noise. This ebook is dedicated to basic difficulties within the generalized method of sign processing in noise according to a doubtless summary suggestion: the advent of an extra noise resource that doesn't hold any information regarding the sign with a purpose to enhance the qualitative functionality of advanced sign processing platforms. Theoretical and experimental stories performed by means of the writer bring about the belief that the proposed generalized method of sign processing in noise permits us to formulate a decision-making rule according to the determi state of the together adequate information of the suggest and variance of the chance functionality (or functional). Classical and glossy sign detection theories let us outline in simple terms the enough statistic of the suggest of the possibility functionality (or functional). The presence of extra information regarding the statistical features of the like lihood functionality (or sensible) ends up in better-quality sign detection compared to the optimum sign detection algorithms of classical and smooth theories.
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Extra resources for Signal Detection Theory
Example text
Note that the design and construction of the optimal detector of signals in noise by anyone of the above-mentioned criteria require the availability of a priori data for construction of the likelihood functions WN I (X HI) and WN(X I Ho). We suggest that the deterministic signal a(t) in noise n(t) must be detected using the observed sample X = (Xl, X 2 , ••• ,Xn). Then the signal detection algorithm reduces to a comparison of the likelihood ratio with the threshold K. The threshold K is determined by the chosen criterion of optimality of the signal detection in noise.
The Problems of Nonlinear Filtering. 4, 1961 (in Russian). [13] Develet, J. Coherent FDMjFM Telephone Communication. Proc. IRE, Vol. 9, 1962. [14] Middleton, D. Acoustic Signal Detection by Simple Correlator in the Presence of Non-Gaussian Noise. J. Acoust. Soc. , Vol. 34, October, 1962. pp. 1598-1609. [15] Develet, J. A Threshold Criterion for Phase-Lock Demodulations. Proc. IEEE, Vol. 51, XVIII, Pt. 2, 1963. pp. 498-508. 36 CHAPTER 1. CLASSICAL SIGNAL DETECTION THEORY [16] Van Trees, H. The Structure of Efficient Demodulator for Multidimensional Phase Modulated Signals.
14) can be rewritten in terms of Eqs. 1. GAUSSIAN APPROACH 17 where C = canst. 15) shows that there is a need to define the estimations Ai of the random values Ai, which result in a posteriori probability distribution density W N IXi ) has the maximum maximorum or the most probable values Ai are optimal. The algorithm of computing the values Ai making maximal the probability distribution density W N (Ai I Xi) defines the structure of the optimal detector. 16) Ak=Ak is used for definition of the estimations Ai.