Physics

Array processing

Subspace topology

Mathematics

Beamforming

Noise (video)

Algorithm

Image (mathematics)

Parametric statistics

Signal processing

Optics

Statistics

Machine learning

Sampling (signal processing)

Artificial intelligence

Estimation theory

Direction of arrival

Antenna (radio)

Focus (optics)

Programming language

Signal subspace

SIGNAL (programming language)

Computer science

Multidimensional signal processing

Radar

Antenna array

Sensor array

Telecommunications

Detector

IEEE Signal Processing Magazine

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.

Two decades of array signal processing research: the parametric approach