Аннотация
The quintessential goal of sensor array signal processing is the estimation
of parameters by fusing temporal and spatial information, captured
via sampling a wavefield with a set of judiciously placed antenna
sensors. The wavefield is assumed to be generated by a finite number
of emitters, and contains information about signal parameters characterizing
the emitters. A review of the area of array processing is given.
The focus is on parameter estimation methods, and many relevant problems
are only briefly mentioned. We emphasize the relatively more recent
subspace-based methods in relation to beamforming. The article consists
of background material and of the basic problem formulation.Then
we introduce spectral-based algorithmic solutions to the signal parameter
estimation problem. We contrast these suboptimal solutions to parametric
methods. Techniques derived from maximum likelihood principles as
well as geometric arguments are covered. Later, a number of more
specialized research topics are briefly reviewed. Then, we look at
a number of real-world problems for which sensor array processing
methods have been applied. We also include an example with real experimental
data involving closely spaced emitters and highly correlated signals,
as well as a manufacturing application example.
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