The signal on the left looks like noise, but the signal processing technique known as
spectral density estimation (right) shows that it contains five well-defined frequency components.
According to
Alan V. Oppenheim and
Ronald W. Schafer, the principles of signal processing can be found in the classical
numerical analysis techniques of the 17th century. They further state that the digital refinement of these techniques can be found in the digital
control systems of the 1940s and 1950s.[3]
Signal processing matured and flourished in the 1960s and 1970s, and digital signal processing became widely used with specialized digital signal processor chips in the 1980s.[5]
Definition of a signal
A signal is a
function, where this function is either[6]
deterministic (then one speaks of a deterministic signal) or
Continuous-time signal processing is for signals that vary with the change of continuous domain (without considering some individual interrupted points).
The methods of signal processing include
time domain,
frequency domain, and
complex frequency domain. This technology mainly discusses the modeling of a linear time-invariant continuous system, integral of the system's zero-state response, setting up system function and the continuous time filtering of deterministic signals
Discrete time
Discrete-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.
Analog 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. This technology was a predecessor of digital signal processing (see below), and is still used in advanced processing of gigahertz signals.
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.
Polynomial signal processing is a type of non-linear signal processing, where
polynomial systems may be interpreted as conceptually straightforward extensions of linear systems to the non-linear case.[9]
Statistical
Statistical signal processing is an approach which treats signals as
stochastic processes, utilizing their
statistical properties to perform signal processing tasks.[10] Statistical techniques are widely used in signal processing applications. For example, one can model the
probability distribution of noise incurred when photographing an image, and construct techniques based on this model to
reduce the noise in the resulting image.
Samplers and
analog-to-digital converters for
signal acquisition and reconstruction, which involves measuring a physical signal, storing or transferring it as digital signal, and possibly later rebuilding the original signal or an approximation thereof.
Data mining – for statistical analysis of relations between large quantities of variables (in this context representing many physical signals), to extract previously unknown interesting patterns
^
abBillings, S. A. (2013). Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains. Wiley.
ISBN978-1-119-94359-4.
^Slawinska, J.; Ourmazd, A.; Giannakis, D. (2018). "A New Approach to Signal Processing of Spatiotemporal Data". 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE Xplore. pp. 338–342.
doi:
10.1109/SSP.2018.8450704.
ISBN978-1-5386-1571-3.
S2CID52153144.
^V. John Mathews; Giovanni L. Sicuranza (May 2000). Polynomial Signal Processing. Wiley.
ISBN978-0-471-03414-8.
^Boashash, Boualem, ed. (2003). Time frequency signal analysis and processing a comprehensive reference (1 ed.). Amsterdam: Elsevier.
ISBN0-08-044335-4.