Physics

Pattern recognition (psychology)

Waveform

Mathematics

Algorithm

Signal processing

Statistics

Filter (signal processing)

Basis (linear algebra)

Compressed sensing

Matching pursuit

Time–frequency analysis

Artificial intelligence

Matching (statistics)

Programming language

Geometry

SIGNAL (programming language)

Computer science

Quantum mechanics

Digital signal processing

Speech recognition

Computer hardware

Basis pursuit

Computer vision

Radar

Orthonormal basis

Telecommunications

IEEE Transactions on Signal Processing

The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit defines an adaptive time-frequency transform. They derive a signal energy distribution in the time-frequency plane, which does not include interference terms, unlike Wigner and Cohen class distributions. A matching pursuit isolates the signal structures that are coherent with respect to a given dictionary. An application to pattern extraction from noisy signals is described. They compare a matching pursuit decomposition with a signal expansion over an optimized wavepacket orthonormal basis, selected with the algorithm of Coifman and Wickerhauser see (IEEE Trans. Informat. Theory, vol. 38, Mar. 1992).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Matching pursuits with time-frequency dictionaries