are inherent to nature and most man-made processes that influence
human existence. They are attributed to numerous sources and
typical examples include acoustic, vibration, biological and
financial systems. Before it is possible to process a signal,
it is first necessary to measure or collect the signal data
using an accurate measurement method that does not distort
or limit the signal content in any way.
the measurement, signal processing is then applied to the
measured data in order to extract the information that is
required. At this point, the person carrying out the analysis
has to choose the most appropriate signal processing tool
from a range of options.
to select the right tools, it is not only necessary to have
a clear understanding of the characteristics of the measured
signal i.e. signal class, but also the signal processing technique
and its limitations. Most people are familiar with classical
time and frequency domain methods which often become the method
employed because of habit and availability, even though they
may not be the most suitable choice.
linear time invariant type systems, Fourier transformation
is very effective in breaking down a complex signal into its
individual frequency components, identifying the characteristics
of individual components, their relationships and contribution.
Various filtering and signal averaging methods are usually
available to reduce noise and remove unwanted frequency components.
Similarly, it is possible to identify the characteristics
of physical elements in a system or process by measuring an
input/output relationship or transfer function. For periodic
signal measurements, reasonable analysis accuracy can usually
be achieved without the optimisation of time-record length
processes do not generate the type of signals described above;
these include Evolutionary Broadband Signals and Transient
Signals. Processing these signal types requires a much greater
understanding of signal processing methods and a range of
less known techniques, in order to extract useful information
from the process and provide better sensitivity between events
in the time and frequency domains. In this case, processing
methods may include short time Fourier analysis, time frequency
representation, adaptive filtering such as Kalman, auto regressive
signal modeling, Prony residue techniques and Wavelet analysis.
of these methods is to use the sensitivity of a signal processing
technique to identify certain characteristics in the signal
by the process of decomposition, then reconstructing the signal
minus the components that confuse the signal identity.
area of importance in signal processing is Parametric Modeling.
In this case, it is possible to derive the mathematical parameters
that describe the signal, system or process. Using information
such as impulse or frequency response or input/output relationships,
it is possible to derive the coefficients of a linear system
and model its behavior.
areas of application of signal processing include the generation
of complex signals and sequences as sources of excitation
for specific processes. Alternatively, complex adaptive algorithms
are included in control loops to enable real-time control
of a process; these include algorithms such as LMS, Filtered
X, Filtered U and hybrid adaptations. Typical examples of
control systems include active noise and vibration control
and positioning guidance systems.