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In many practical signal processing problems, the objective is to estimate from measurements a set of constant parameters upon which the received signals depend. There have been several approaches to such problems including the so-called maximum likelihood (ML) method of Capon (1969) and Burg's maximum entropy (ME) method.
Non-parametric methods for which the signal samples must be evenly spaced in time (records must be complete): Periodogram, the modulus squared of the discrete Fourier transform; Bartlett's method is the average of the periodograms taken of multiple segments of the signal to reduce variance of the spectral density estimate
Pages in category "Statistical signal processing" The following 23 pages are in this category, out of 23 total. This list may not reflect recent changes. B.
In statistics, econometrics, and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it can be used to describe certain time-varying processes in nature, economics, behavior, etc.
In stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analysing time series that appear to be long-memory processes (diverging correlation time, e.g. power-law decaying autocorrelation function) or 1/f noise.
His research interests lie in the areas of stochastic analysis, statistical signal processing and information theory, and their applications in a number of fields including wireless networks, social networks, and smart grid. This research work has attracted over 10,000 citations. [10] He has published a book on Signal Detection and Estimation. [11]
Maximum entropy spectral estimation is a method of spectral density estimation.The goal is to improve the spectral quality based on the principle of maximum entropy.The method is based on choosing the spectrum which corresponds to the most random or the most unpredictable time series whose autocorrelation function agrees with the known values.
Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice Hall. ISBN 0-13-042268-1. Moon, Todd K. (2000). Mathematical Methods and Algorithms for Signal Processing. Prentice-Hall. ISBN 0-201-36186-8