Statistical signal processing is an approach which treats signals as stochastic processes, utilizing their statistical properties to perform signal processing tasks. Polynomial signal processing is a type of non-linear signal processing, where polynomial systems may be interpreted as conceptually straight forward extensions of linear systems to the non-linear case. Nonlinear systems can produce highly complex behaviors including bifurcations, chaos, harmonics, and subharmonics which cannot be produced or analyzed using linear methods. Nonlinear signal processing involves the analysis and processing of signals produced from nonlinear systems and can be in the time, frequency, or spatio-temporal domains. Examples of algorithms are the fast Fourier transform (FFT), finite impulse response (FIR) filter, Infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters. Other typical operations supported by the hardware are circular buffers and lookup tables. Typical arithmetical operations include fixed-point and floating-point, real-valued and complex-valued, multiplication and addition. Processing is done by general-purpose computers or by digital circuits such as ASICs, field-programmable gate arrays or specialized digital signal processors (DSP chips).
The concept of discrete-time signal processing also refers to a theoretical discipline that establishes a mathematical basis for digital signal processing, without taking quantization error into consideration.ĭigital signal processing is the processing of digitized discrete-time sampled signals. This technology was a predecessor of digital signal processing (see below), and is still used in advanced processing of gigahertz signals. This technology mainly discusses the modeling of linear time-invariant continuous system, integral of the system's zero-state response, setting up system function and the continuous time filtering of deterministic signalsĭiscrete-time signal processing is for sampled signals, defined only at discrete points in time, and as such are quantized in time, but not in magnitude.Īnalog discrete-time signal processing is a technology based on electronic devices such as sample and hold circuits, analog time-division multiplexers, analog delay lines and analog feedback shift registers. The methods of signal processing include time domain, frequency domain, and complex frequency domain. Nonlinear circuits include compandors, multipliers ( frequency mixers, voltage-controlled amplifiers), voltage-controlled filters, voltage-controlled oscillators, and phase-locked loops.Ĭontinuous-time signal processing is for signals that vary with the change of continuous domain (without considering some individual interrupted points). The former are, for instance, passive filters, active filters, additive mixers, integrators, and delay lines. This involves linear electronic circuits as well as nonlinear ones. Analog signal processing is for signals that have not been digitized, as in most 20th-century radio, telephone, radar, and television systems.
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