On the uncertainty inequality as applied to discrete signals

Given a continuous-time bandlimited signal, the Shannon sampling theorem provides an interpolation scheme for exactly reconstructing it from its discrete samples. We analyze the relationship between concentration (or compactness )i n thetemporal/spectral domains of the (i) continuous-time and (ii) discrete-time signals. The former is governed by the Heisenberg uncertainty inequality which prescribes a lower bound on the product of effective temporal and spectral spreads of the signal. On the other hand, the discrete-time counterpart seems to exhibit some strange properties, and this provides motivation for the present paper. We consider the following problem: for a bandlimited signal, can the uncertainty inequality be expressed in terms of the samples, using the standard definitions of the temporal and spectral spreads of the signal? In contrast with the results of the literature, we present a new approach to solve this problem. We also present a comparison of the results obtained using the proposed definitions with those available in the literature.

[1]  Petar M. Djuric,et al.  Time-frequency localization for sequences , 1992, [1992] Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis.

[2]  The Indian Institute of Science. , 1908, Nature.

[3]  Victor E. DeBrunner,et al.  Resolution in time-frequency , 1999, IEEE Trans. Signal Process..

[4]  Amir Dembo,et al.  Information theoretic inequalities , 1991, IEEE Trans. Inf. Theory.

[5]  John J. Benedetto,et al.  Applied and numerical harmonic analysis , 1997 .

[6]  A. Papoulis Signal Analysis , 1977 .

[7]  Milos Doroslovacki,et al.  Product of second moments in time and frequency for discrete-time signals and the uncertainty limit , 1998, Signal Process..

[8]  Victor E. DeBrunner,et al.  The optimal transform for the discrete Hirschman uncertainty principle , 2001, IEEE Trans. Inf. Theory.

[9]  L. Debnath Wavelets and Signal Processing , 2012 .

[10]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[11]  Victor E. DeBrunner,et al.  Using a new uncertainty measure to determine optimal bases for signal representations , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[12]  L. Debnath Wavelet Transforms and Time-Frequency Signal Analysis , 2001 .

[13]  D. Donoho,et al.  Uncertainty principles and signal recovery , 1989 .

[14]  P. Maass,et al.  The Affine uncertainty principle in one and two dimensions , 1995 .

[15]  P. Vilbe,et al.  On the uncertainty principle in discrete signals , 1992 .

[16]  G. Folland,et al.  The uncertainty principle: A mathematical survey , 1997 .

[17]  F. Harris On the use of windows for harmonic analysis with the discrete Fourier transform , 1978, Proceedings of the IEEE.