By Debasis Kundu

Signal processing may possibly commonly be thought of to contain the restoration of knowledge from actual observations. The acquired sign is generally disturbed through thermal, electric, atmospheric or intentional interferences. end result of the random nature of the sign, statistical suggestions play an immense function in interpreting the sign. facts can be utilized in the formula of the fitting versions to explain the habit of the process, the advance of applicable ideas for estimation of version parameters and the overview of the version performances. Statistical sign processing essentially refers back to the research of random signs utilizing applicable statistical thoughts. the most goal of this booklet is to introduce diverse sign processing versions which were utilized in interpreting periodic information, and diversified statistical and computational matters all for fixing them. We talk about intimately the sinusoidal frequency version which has been used greatly in reading periodic information occuring in numerous fields. we've attempted to introduce diversified linked types and better dimensional statistical sign processing types which were additional mentioned within the literature. diverse genuine information units were analyzed to demonstrate how assorted types can be utilized in perform. a number of open difficulties were indicated for destiny research.

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Extra info for Statistical Signal Processing: Frequency Estimation

Sample text

For a better understanding, we briefly explain the EM algorithm here. Let Y denote the observed (may be incomplete) data with the probability density function f Y (y; θ ) indexed by the parameter vector θ ∈ ⊂ Rk and let X denote the complete data vector related to Y by H (X) = Y, 30 3 Estimation of Frequencies where H (·) is a many-to-one non-invertible function. Therefore, the density function of X, say f X (x, θ ), can be written as f X (x; θ ) = f X|Y=y (x; θ ) f Y (y; θ ) ∀H (x) = y. 30) Here f X|Y=y (x; θ ) is the conditional probability density function of X, given Y = y.

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1. 25). The idea behind the EVLP method is as follows: Consider an (n − 2 p) × n data matrix Y D as 20 3 Estimation of Frequencies ⎡ ⎤ · · · y(2 p + 1) ⎢ ⎥ .. YD = ⎣ ⎦. . y(n − 2 p) · · · y(n) y(1) .. 9) If {X (t)} is absent, Rank(Y D ) = Rank(YTD Y D /n) = 2 p. It implies that the symmetric matrix (YTD Y D /n) has an eigenvalue zero with multiplicity one. Therefore, there exists an eigenvector g = (g0 , . 10) has roots at e±iω1 , . , e±iω p . Using this idea, Bai et al. 1, from the symmetric matrix (YTD Y D /n) obtain the normalized eigenvector g = (g0 , .