Cyclic LTI systems in digital signal processing

Cyclic signal processing refers to situations where all the time indices are interpreted modulo some integer L. In such cases, the frequency domain is defined as a uniform discrete grid (as in L-point DFT). This offers more freedom in theoretical as well as design aspects. While circular convolution has been the centerpiece of many algorithms in signal processing for decades, such freedom, especially from the viewpoint of linear system theory, has not been studied in the past. In this paper, we introduce the fundamentals of cyclic multirate systems and filter banks, presenting several important differences between the cyclic and noncyclic cases. Cyclic systems with allpass and paraunitary properties are studied. The paraunitary interpolation problem is introduced, and it is shown that the interpolation does not always succeed. State-space descriptions of cyclic LTI systems are introduced, and the notions of reachability and observability of state equations are revisited. It is shown that unlike in traditional linear systems, these two notions are not related to the system minimality in a simple way. Throughout the paper, a number of open problems are pointed out from the perspective of the signal processor as well as the system theorist.

[1]  Maurice Bellanger,et al.  Digital processing of signals , 1989 .

[2]  Hitoshi Kiya,et al.  Property of circular convolution for subband image coding , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  Mark J. T. Smith,et al.  Analysis/synthesis techniques for subband image coding , 1990, IEEE Trans. Acoust. Speech Signal Process..

[4]  A. W. M. van den Enden,et al.  Discrete Time Signal Processing , 1989 .

[5]  Thomas Kailath,et al.  Linear Systems , 1980 .

[6]  Ajit S. Bopardikar,et al.  Perfect reconstruction circular convolution filter banks and their application to the implementation of bandlimited discrete wavelet transforms , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Ajit S. Bopardikar,et al.  PRCC filter banks: theory, implementation, and application , 1997, Optics & Photonics.

[8]  Chi-Tsong Chen,et al.  Linear System Theory and Design , 1995 .

[9]  Veyis Nuri,et al.  Size-limited filter banks for subband image compression , 1995, IEEE Trans. Image Process..

[10]  Ricardo L. de Queiroz,et al.  On reconstruction methods for processing finite-length signals with paraunitary filter banks , 1995, IEEE Trans. Signal Process..

[11]  Hitoshi Kiya,et al.  A parallel AR spectral estimation using a new class of filter bank , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[12]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.

[13]  Robert L. Grossman,et al.  Wavelet transforms associated with finite cyclic groups , 1993, IEEE Trans. Inf. Theory.

[14]  Mark J. T. Smith,et al.  Exact reconstruction techniques for tree-structured subband coders , 1986, IEEE Trans. Acoust. Speech Signal Process..

[15]  Henrique S. Malvar,et al.  Signal processing with lapped transforms , 1992 .

[16]  P. P. Vaidyanathan Results on cyclic signal processing systems , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[17]  P. P. Vaidyanathan,et al.  Theory of cyclic filter banks , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[18]  P. P. Vaidyanathan,et al.  Cyclic LTI systems and the paraunitary interpolation problem , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[19]  R. Haddad,et al.  Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets , 1992 .

[20]  K.R. Ramakrishnan,et al.  Design of two-channel linear phase orthogonal cyclic filterbanks , 1998, IEEE Signal Processing Letters.

[21]  P. P. Vaidyanathan,et al.  Efficient reconstruction of band-limited sequences from nonuniformly decimated versions by use of polyphase filter banks , 1990, IEEE Trans. Acoust. Speech Signal Process..

[22]  M.G. Larimore,et al.  Digital filters: Analysis and design , 1981, Proceedings of the IEEE.

[23]  H. J. Nussbaumer,et al.  Digital filtering using polynomial transforms , 1977 .

[24]  Gunnar Karlsson,et al.  Extension of finite length signals for sub-band coding , 1989 .

[25]  Y. Meyer,et al.  Wavelets and Filter Banks , 1991 .

[26]  Stephen A. Dyer,et al.  Digital signal processing , 2018, 8th International Multitopic Conference, 2004. Proceedings of INMIC 2004..