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Statistical signal processing – analyzing and extracting information from signals and noise based on their stochastic properties; Linear time-invariant system theory, and transform theory; Polynomial signal processing – analysis of systems which relate input and output using polynomials; System identification [8] and classification ...
The term video refers to the resulting signal being appropriate for display on a cathode ray tube, or "video screen". The role of the constant false alarm rate circuitry is to determine the power threshold above which any return can be considered to probably originate from a target as opposed to one of the spurious sources.
Technical Program Chair: Louis L. Scharf 970 257 1981 30 March-1 April Atlanta, GA, USA General Chair: Ronald W. Schafer Technical Program Chair: Russell M. Mersereau 950 295 1982 3-5 May Paris, France General Chair: Claude Gueguen Technical Program Chair: Maurice Bellanger 1653 522 1983 14-16 April Boston, MA, USA
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.
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 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.
In statistical signal processing, the goal of spectral density estimation (SDE) or simply spectral estimation is to estimate the spectral density (also known as the power spectral density) of a signal from a sequence of time samples of the signal. [1] Intuitively speaking, the spectral density characterizes the frequency content of
The general ARMA model was described in the 1951 thesis of Peter Whittle, who used mathematical analysis (Laurent series and Fourier analysis) and statistical inference. [ 12 ] [ 13 ] ARMA models were popularized by a 1970 book by George E. P. Box and Jenkins, who expounded an iterative ( Box–Jenkins ) method for choosing and estimating them.