Multirate processing of digital signals
Advanced topics in signal processing
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet transforms and filter banks
Wavelets: a tutorial in theory and applications
Multirate Digital Signal Processing
Multirate Digital Signal Processing
Introduction to Discrete-Time Signals and Systems
Introduction to Discrete-Time Signals and Systems
Random Data: Analysis and Measurement Procedures
Random Data: Analysis and Measurement Procedures
Waveform and image compression using the Burrows Wheeler transform and the wavelet transform
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
On-line identification of echo-path impulse responses by Haar-wavelet-based adaptive filter
ICASSP '95 Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02
Wavelet-based linear system modeling and adaptive filtering
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A shift-invariant discrete wavelet transform
IEEE Transactions on Signal Processing
Time-varying system identification and model validation usingwavelets
IEEE Transactions on Signal Processing
Technical Communique: Wavelet-Based Identification of Linear Discrete-Time Systems: Robustness Issue
Automatica (Journal of IFAC)
Auditory representations of acoustic signals
IEEE Transactions on Information Theory - Part 2
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Parametric CARMA model identification based on WTLMS algorithm
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
On the choice of filter bank parameters for wavelet-packet identification of dynamic systems
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
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We describe the use of the discrete wavelet transform (DWT) for non-parametric linear time-invariant system identification. Identification is achieved by using a test excitation to the system under test (SUT) that also acts as the analyzing function for the DWT of the SUT's output, so as to recover the impulse response. The method uses as excitation any signal that gives an orthogonal inner product in the DWT at some step size (that cannot be 1). We favor wavelet scaling coefficients as excitations, with a step size of 2. However, the system impulse or frequency response can then only be estimated at half the available number of points of the sampled output sequence, introducing a multirate problem that means we have to 'oversample' the SUT output. The method has several advantages over existing techniques, e.g., it uses a simple, easy to generate excitation, and avoids the singularity problems and the (unbounded) accumulation of round-off errors that can occur with standard techniques. In extensive simulations, identification of a variety of finite and infinite impulse response systems is shown to be considerably better than with conventional system identification methods.