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Sparse Sensing With Co-Prime Samplers and Arrays

IEEE Transactions on Signal Processing · 2010 · Vol. 59(2) · pp. 573–586
P. P. VaidyanathanPiya Pal

Abstract

This paper considers the sampling of temporal or spatial wide sense stationary (WSS) signals using a co-prime pair of sparse samplers. Several properties and applications of co-prime samplers are developed. First, for uniform spatial sampling with M and N sensors where M and N are co-prime with appropriate interelement spacings, the difference co-array has O(MN) freedoms which can be exploited in beamforming and in direction of arrival estimation. An M -point DFT filter bank and an N-point DFT filter bank can be used at the outputs of the two sensor arrays and their outputs combined in such a way that there are effectively MN bands (i.e., MN narrow beams with beamwidths proportional to 1/MN), a result following from co-primality. The ideas are applicable to both active and passive sensing, though the details and tradeoffs are different. Time domain sparse co-prime samplers also generate a time domain co-array with O(MN) freedoms, which can be used to estimate the autocorrelation at much finer lags than the sample spacings. This allows estimation of power spectrum of an arbitrary signal with a frequency resolution proportional to 2π/(MNT) even though the pairs of sampled sequences x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> (NTn) and x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> (MTn) in the time domain can be arbitrarily sparse - in fact from the sparse set of samples x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> (NTn) and x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> (MTn) one can estimate O(MN) frequencies in the range |ω| <; π/T. It will be shown that the co-array based method for estimating sinusoids in noise offers many advantages over methods based on the use of Chinese remainder theorem and its extensions. Examples are presented throughout to illustrate the various concepts.

Direction-of-Arrival Estimation TechniquesSpeech and Audio ProcessingAdvanced Adaptive Filtering TechniquesPrime (order theory)Sampling (signal processing)OversamplingComputer scienceAutocorrelationAlgorithmBeamformingCoprime integersFilter (signal processing)Mathematics
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References
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IEEE Transactions on Signal Processing · 2010 · 1,937 citations
Multiple emitter location and signal parameter estimation
IEEE Transactions on Antennas and Propagation · 1986 · 14,098 citations
Minimum-redundancy linear arrays
IEEE Transactions on Antennas and Propagation · 1968 · 1,296 citations
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