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Browsing Faculty Publications by Subject "adaptive filters"
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Item Open Access A modified block FTF adaptive algorithm with applications to underwater target detection(Colorado State University. Libraries, 1996) Hasan, Mohammed A., author; Azimi-Sadjadi, Mahmood R., author; IEEE, publisherIn this paper, the problem of weighted block recursive least squares (RLS) adaptive filtering is formulated in the context of block fast transversal filter (FTF) algorithm. This “modified block FTF algorithm” is derived by modifying the constrained block-LS cost function to guarantee global optimality. This new soft-constrained algorithm provides an efficient way of transferring weight information between blocks of data. The tracking ability of the algorithm can be controlled by varying the block length and/or a soft constrained parameter. This algorithm is computationally more efficient compared with other LS-based schemes. The effectiveness of this algorithm is tested on a real-life problem dealing with underwater target identification from acoustic backscatter. The process involves the identification of the presence of resonance in the acoustic backscatter from a target of unknown shape submerged in water.Item Open Access Data adaptive rank-shaping methods for solving least squares problems(Colorado State University. Libraries, 1995) Scharf, Louis L., author; Thorpe, Anthony J., author; IEEE, publisherThere are two types of problems in the theory of least squares signal processing: parameter estimation and signal extraction. Parameter estimation is called "inversion" and signal extraction is called "filtering." In this paper, we present a unified theory of rank shaping for solving overdetermined and underdetermined versions of these problems. We develop several data-dependent rank-shaping methods and evaluate their performance. Our key result is a data-adaptive Wiener filter that automatically adjusts its gains to accommodate realizations that are a priori unlikely. The adaptive filter dramatically outperforms the Wiener filter on atypical realizations and just slightly underperforms it on typical realizations. This is the most one can hope for in a data-adaptive filter.Item Open Access Estimation and identification for 2-D block Kalman filtering(Colorado State University. Libraries, 1991) Azimi-Sadjadi, Mahmood R., author; IEEE, publisherThis correspondence is concerned with the development of a recursive identification and estimation procedure for 2-D block Kalman filtering. The recursive identification scheme can be used on-line to update the image model parameters at each iteration based upon the local statistics within a block of the observed noisy image. The covariance matrix of the driving noise can also be estimated at each iteration of this algorithm. A recursive procedure is given for computing the parameters of the higher order models. Simulation results are also provided.Item Open Access Isolation of resonance in acoustic backscatter from elastic targets using adaptive estimation schemes(Colorado State University. Libraries, 1995) Wilbur, JoEllen, author; Azimi-Sadjadi, Mahmood R., author; Dobeck, Gerald J., author; IEEE, publisherThe problem of underwater target detection and classification from acoustic backscatter is the central focus of this paper. It has been shown that at certain frequencies the acoustic backscatter from elastic targets exhibits certain resonance behavior which closely relates to the physical properties of the target such as dimension, thickness, and composition. Several techniques in both the time domain and frequency domain have been developed to characterize the resonance phenomena in acoustic backscatter from spherical or cylindrical thin shells. The purpose of this paper is to develop an automated approach for identifying the presence of resonance in the acoustic backscatter from an unknown target by isolating the resonance part from the specular contribution. An adaptive transversal filter structure is used to estimate the specular part of the backscatter and consequently the error signal would provide an estimate of the resonance part. An important aspect of this scheme lies in the fact that it does not require an underlying model for the elastic return. The adaptation rule is based upon fast Recursive Least Squares (RLS) learning. The approach taken in this paper is general in the sense that it can be applied to targets of unknown geometry and thickness and, further, does not require any a priori information about the target and/or the environment. Test results on acoustic data are presented which indicate the effectiveness of the proposed approach.Item Open Access Two-dimensional adaptive block Kalman filtering of SAR imagery(Colorado State University. Libraries, 1991) Azimi-Sadjadi, Mahmood R., author; Bannour, Sami, author; IEEE, publisherSpeckle effects are commonly observed in synthetic aperture radar (SAR) imagery. In airborne SAR systems the effect of this degradation reduces the accuracy of detection substantially. Thus, the elimination of this noise is an important task in SAR imaging systems. In this paper a new method for speckle noise removal is introduced using 2-D adaptive block Kalman filtering (ABKF). The image process is represented by an autoregressive (AR) model with nonsymmetric half-plane (NSHP) region of support. New 2-D Kalman filtering equations are derived which take into account not only the effect of speckles as a multiplicative noise but also those of the additive receiver thermal noise and the blur. This method assumes local stationarity within a processing window, whereas the image can be assumed to be globally nonstationary. A recursive identification process using the stochastic Newton approach is also proposed which can be used on-line to estimate the filter parameters based upon the information within each new block of the image. Simulation results on several images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise as well as that of the additive noise.Item Open Access Two-dimensional block diagonal LMS adaptive filtering(Colorado State University. Libraries, 1994) Pan, Hongye, author; Azimi-Sadjadi, Mahmood R., author; IEEE, publisherThis paper is concerned with the development of a two-dimensional (2-D) adaptive filters using the block diagonal least mean squared (BDLMS) method. In this adaptive filtering scheme the image is scanned and processed block by block in a diagonal fashion, and the filter weights are adjusted once per block rather than once per pixel. The diagonal scanning is adopted to avoid the problems inherent in the 1-D standard scanning schemes and to account for the correlations in two directions. The weight updating equation for 2-D BDLMS is derived and the convergence properties of the algorithms are investigated. Simulation results that indicate the effectiveness of the 2-D BDLMS when used for image enhancement, estimation, and detection applications are presented.Item Open Access Two-dimensional recursive parameter identification for adaptive Kalman filtering(Colorado State University. Libraries, 1991) Azimi-Sadjadi, Mahmood R., author; Bannour, Sami, author; IEEE, publisherThis paper is concerned with the development of a 2-D adaptive Kalman filtering by recursive adjustment of the parameters of an autoregressive (AR) image model with non symmetric half-plane (NSHP) region of support. The image and degradation models are formulated in a 2-D state-space model, for which the relevant 2-D Kalman filtering equations are given. The recursive parameter identification is achieved using the extension of the stochastic Newton approach to the 2-D case. This process can be implemented on-line to estimate the image model parameters based upon the local statistics in every processing window. Simulation results for removing an additive noise from a degraded image are also presented.