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Browsing Faculty Publications by Subject "adaptive filtering"
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Item Open Access A multistage representation of the Wiener filter based on orthogonal projections(Colorado State University. Libraries, 1998) Scharf, Louis L., author; Reed, Irving S., author; Goldstein, J. Scott, author; IEEE, publisherThe Wiener filter is analyzed for stationary complex Gaussian signals from an information-theoretic point of view. A dual-port analysis of the Wiener filter leads to a decomposition based on orthogonal projections and results in a new multistage method for implementing the Wiener filter using a nested chain of scalar Wiener filters. This new representation of the Wiener filter provides the capability to perform an information-theoretic analysis of previous, basis-dependent, reduced-rank Wiener filters. This analysis demonstrates that the recently introduced cross-spectral metric is optimal in the sense that it maximizes mutual information between the observed and desired processes. A new reduced-rank Wiener filter is developed based on this new structure which evolves a basis using successive projections of the desired signal onto orthogonal, lower dimensional subspaces. The performance is evaluated using a comparative computer analysis model and it is demonstrated that the low-complexity multistage reduced-rank Wiener filter is capable of outperforming the more complex eigendecomposition-based methods.Item Open Access A new time delay estimation in subbands for resolving multiple specular reflections(Colorado State University. Libraries, 1998) Dobeck, Gerald J., author; Wilbur, JoEllen, author; Charleston, Sonia, author; Azimi-Sadjadi, Mahmood R., author; IEEE, publisherIn this correspondence, a new time delay estimation procedure is proposed using the multiresolution analysis framework through a discrete wavelet transform (DWT). Once the signals are decomposed, the time delays are estimated iteratively in each sub-band using two different adaptation mechanisms that minimize the mean squared error (MSE) between the reference and primary signals in the corresponding sub-band and level. The localization of the minima of the MSE curves at different levels and subbands is used in order to arrive at the time delay estimates. The proposed scheme is then applied to a real-life problem of underwater target detection from the acoustic backscttered data.Item Open Access Interference cancellation in respiratory sounds via a multiresolution joint time-delay and signal-estimation scheme(Colorado State University. Libraries, 1997) González-Camarena, Ramon, author; Azimi-Sadjadi, Mahmood R., author; Charleston, Sonia, author; IEEE, publisherThis paper is concerned with the problem of cancellation of heart sounds from the acquired respiratory sounds using a new joint time-delay and signal-estimation (JTDSE) procedure. Multiresolution discrete wavelet transform (DWT) is first applied to decompose the signals into several subbands. To accurately separate the heart sounds from the acquired respiratory sounds, time-delay estimation (TDE) is performed iteratively in each subband using two adaptation mechanisms that minimize the sum of squared errors between these signals. The time delay is updated using a nonlinear adaptation, namely the Levenberg-Marquardt (LM) algorithm, while the function of the other adaptive system-which uses the block fast transversal filter (BFTF)—is to minimize the mean squared error between the outputs of the delay estimator and the adaptive filter. The proposed methodology possesses a number of key benefits such as the incorporation of multiple complementary information at different subbands, robustness in presence of noise, and accuracy in TDE. The scheme is applied to several cases of simulated and actual respiratory sounds under different conditions and the results are compared with those of the standard adaptive filtering. The results showed the promise of the scheme for the TDE and subsequent interference cancellation.Item Open Access Underwater target detection using multichannel subband adaptive filtering and high-order correlation schemes(Colorado State University. Libraries, 2000) Dobeck, Gerald J., author; Wilbur, JoEllen, author; Azimi-Sadjadi, Mahmood R., author; Yuan, Chunhua, author; IEEE, publisherIn this paper, new pre- and post-processing schemes are developed to process shallow-water sonar data to improve the accuracy of target detection. A multichannel subband adaptive filtering is applied to preprocess the data in order to isolate the potential target returns from the acoustic backscattered signals and improve the signal-to-reverberation ratio. This is done by estimating the time delays associated with the reflections in different subbands. The preprocessed results are then beamformed to generate an image for each ping of the sonar. The testing results on both the simulated and real data revealed the efficiency of this scheme in time-delay estimation and its capability in removing most of the competing reverberations and noise. To improve detection rate while significantly minimizing the incident of false detections, a high-order correlation (HOC) method for postprocessing the beamformed images is then developed. This method determines the consistency in occurrence of the target returns in several consecutive pings. The application of the HOC process to the real beamformed sonar data showed the ability of this method for removing the clutter and at the same time boosting the target returns in several consecutive pings. The algorithm is simple, fast, and easy to implement.Item Open Access Wiener filters in canonical coordinates for transform coding, filtering, and quantizing(Colorado State University. Libraries, 1998) Thomas, John K., author; Scharf, Louis L., author; IEEE, publisherCanonical correlations are used to decompose the Wiener filter into a whitening transform coder, a canonical filter, and a coloring transform decoder. The outputs of the whitening transform coder are called canonical coordinates; these are the coordinates that are reduced in rank and quantized in our finite-precision version of the Gauss-Markov theorem. Canonical correlations are, in fact, cosines of the canonical angles between a source vector and a measurement vector. They produce new formulas for error covariance, spectral flatness, and entropy.