Dissertations / Theses on the topic 'Adaptive filtering'
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1
Haglund, Leif. "Adaptive Multidimensional Filtering." Doctoral thesis, Linköpings universitet, Bildbehandling, 1991. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54339.
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This thesis contains a presentation and an analysis of adaptive filtering strategies for multidimensional data. The size, shape and orientation of the flter are signal controlled and thus adapted locally to each neighbourhood according to a predefined model. The filter is constructed as a linear weighting of fixed oriented bandpass filters having the same shape but different orientations. The adaptive filtering methods have been tested on both real data and synthesized test data in 2D, e.g. still images, 3D, e.g. image sequences or volumes, with good results. In 4D, e.g. volume sequences, the algorithm is given in its mathematical form. The weighting coefficients are given by the inner products of a tensor representing the local structure of the data and the tensors representing the orientation of the filters. The procedure and lter design in estimating the representation tensor are described. In 2D, the tensor contains information about the local energy, the optimal orientation and a certainty of the orientation. In 3D, the information in the tensor is the energy, the normal to the best ftting local plane and the tangent to the best fitting line, and certainties of these orientations. In the case of time sequences, a quantitative comparison of the proposed method and other (optical flow) algorithms is presented. The estimation of control information is made in different scales. There are two main reasons for this. A single filter has a particular limited pass band which may or may not be tuned to the different sized objects to describe. Second, size or scale is a descriptive feature in its own right. All of this requires the integration of measurements from different scales. The increasing interest in wavelet theory supports the idea that a multiresolution approach is necessary. Hence the resulting adaptive filter will adapt also in size and to different orientations in different scales.
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Adriannse, Robert. "Adaptive local statistics filtering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq21530.pdf.
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Chambers, Brian D. "Adaptive Bayesian information filtering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0007/MQ45945.pdf.
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Rangarao, Kaluri Venkata. "Adaptive digital notch filtering." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26345.
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Xie, Bei. "Partial Update Adaptive Filtering." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/26670.
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Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and
beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity (O(N)) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, their high computational complexity ($O(N^2)$) makes them unsuitable for many real-time applications. A well-known approach
to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the time. An analysis for different PU adaptive filter algorithms is necessary and meaningful. The deficient-length adaptive filter addresses a situation in system identification where the length of the estimated filter is shorter than the length of the actual unknown system. It is related to the partial update adaptive filter, but has different performance. It can be viewed as a PU adaptive filter, in that the deficient-length adaptive filter also updates part of the weight vector. However, it updates the same part of the weight vector for each iteration, while the partial update adaptive filter updates a different part of the weight vector for each iteration.
In this work, basic PU methods are applied to the adaptive filter algorithms which have not been fully addressed in the literature, including CG, EDS, and Constant Modulus Algorithm (CMA) based algorithms. A new PU method, the selective-sequential method, is developed for LSCMA. Mathematical analysis is shown including
convergence condition, steady-state performance, and tracking performance. Computer simulation
with proper examples is also shown to further help study the performance. The performance is compared among different PU methods or among
different adaptive filtering algorithms. Computational complexity is calculated for each PU method and each adaptive filter algorithm. The deficient-length RLS and EDS are also analyzed and compared to the performance of the PU adaptive filter.
In this dissertation, basic partial-update methods are applied to adaptive filter algorithms including CMA1-2, NCMA, Least Squares CMA (LSCMA), EDS, and CG. A new PU method, the selective-sequential method, is developed for LSCMA. Mathematical derivation and performance analysis are provided including convergence condition, steady-state mean and mean-square performance for a time-invariant system. The steady-state mean and mean-square performance are also presented for a time-varying system. Computational complexity is calculated for each adaptive filter algorithm. Numerical examples are shown to compare the computational complexity of the PU adaptive filters with the full-update filters. Computer simulation examples, including system identification and channel equalization, are used to demonstrate the mathematical analysis and show the performance of PU adaptive filter algorithms. They also show the convergence performance of PU adaptive filters. The performance is compared between the original adaptive filter algorithms and different partial-update methods. The performance is also compared among similar PU least-squares adaptive filter algorithms, such as PU RLS, PU CG, and PU EDS. Deficient-length RLS and EDS are studied. The performance of the deficient-length filter is also compared with the partial update filter. In addition to the generic applications of system identification and channel equalization, two special applications of using partial update adaptive filters are also presented. One application is using PU adaptive filters to detect Global System for Mobile Communication (GSM) signals in a local GSM system using the Open Base Transceiver Station (OpenBTS) and Asterisk Private Branch Exchange (PBX). The other application is using PU adaptive filters to do image compression in a system combining hyperspectral image compression and classification.
Overall, the PU adaptive filters can usually achieve comparable performance to the full-update filters while reducing the computational complexity significantly. The PU adaptive filters can achieve similar steady-state MSE to the full-update filters. Among different PU methods, the MMax method has a convergence rate very close to the full-update method. The sequential and stochastic methods converge slower than the MMax method. However, the MMax method does not always perform well with the LSCMA algorithm. The sequential LSCMA has the best performance among the PU LSCMA algorithms. The PU CMA may perform better than the full-update CMA in tracking a time-varying system. The MMax EDS can converge faster than the MMax RLS and CG. It can converge to the same steady-state MSE as the MMax RLS and CG, while having a lower computational complexity. The PU LMS and PU EDS can also perform a little better in a system combining hyperspectral image compression and classification.Ph. D.
6
Kshonze, Kristopher. "Adaptive filtering with systolic arrays." Thesis, University of Ottawa (Canada), 1988. http://hdl.handle.net/10393/5456.
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Fertig, Louis B. "Dual forms for constrained adaptive filtering." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/15642.
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Baykal, Buyurman. "Underdetermined recursive least-squares adaptive filtering." Thesis, Imperial College London, 1995. http://hdl.handle.net/10044/1/7790.
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Faghih, Farshad. "Adaptive wavelet-based noise filtering techniques." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ38627.pdf.
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Wilstrup, Steven L. "Adaptive algorithms for two dimensional filtering." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/22855.
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Hawes, Anthony H. "Least squares and adaptive multirate filtering." Thesis, Monterey, California. Naval Postgraduate School, 2012.
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Approved for public release; distribution in unlimited.This thesis addresses the problem of estimating a random process from two observed signals sampled at different rates. The case where the low-rate observation has a higher signal-to- noise ratio than the high-rate observation is addressed. Both adaptive and non-adaptive filtering techniques are explored. For the non-adaptive case, a multirate version of the Wiener-Hopf optimal filter is used for estimation. Three forms of the filter are described. It is shown that using both observations with this filter achieves a lower mean-squared error than using either sequence alone. Furthermore, the amount of training data to solve for the filter weights is comparable to that needed when using either sequence alone. For the adaptive case, a multirate version of the LMS adaptive algorithm is developed. Both narrowband and broadband interference are removed using the algorithm in an adaptive noise cancellation scheme. The ability to remove interference at the high rate using observations taken at the low rate without the high-rate observations is demonstrated.
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Nambiar, Raghu. "Learning algorithms for adaptive digital filtering." Thesis, Durham University, 1993. http://etheses.dur.ac.uk/5544/.
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In this thesis, we consider the problem of parameter optimisation in adaptive digital filtering. Adaptive digital filtering can be accomplished using both Finite Impulse Response (FIR) filters and Infinite Impulse Response Filters (IIR) filters. Adaptive FIR filtering algorithms are well established. However, the potential computational advantages of IIR filters has led to an increase in research on adaptive IIR filtering algorithms. These algorithms are studied in detail in this thesis and the limitations of current adaptive IIR filtering algorithms are identified. New approaches to adaptive IIR filtering using intelligent learning algorithms are proposed. These include Stochastic Learning Automata, Evolutionary Algorithms and Annealing Algorithms. Each of these techniques are used for the filtering problem and simulation results are presented showing the performance of the algorithms for adaptive IIR filtering. The relative merits and demerits of the different schemes are discussed. Two practical applications of adaptive IIR filtering are simulated and results of using the new adaptive strategies are presented. Other than the new approaches used, two new hybrid schemes are proposed based on concepts from genetic algorithms and annealing. It is shown with the help of simulation studies, that these hybrid schemes provide a superior performance to the exclusive use of any one scheme.
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Pasquato, Lorenzo. "Adaptive filtering with balanced model truncation." Thesis, University of Westminster, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251702.
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Zhahir, Md Amzari. "Adaptive filtering applications to satellite navigation." Thesis, Queen Mary, University of London, 2010. http://qmro.qmul.ac.uk/xmlui/handle/123456789/364.
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Differential Global Navigation Satellite Systems employ the extended Kalman filter to estimate the reference position error. High accuracy integrated navigation systems have the ability to mix traditional inertial sensor outputs with navigation satellite based position information and can be used to develop high accuracy landing systems for aircraft. This thesis considers a host of estimation problems associated with aircraft navigation systems that currently rely on the extended Kalman filter and proposes to use a nonlinear estimation algorithm, the unscented Kalman filter (UKF) that does not rely on Jacobian linearisation. The objective is to develop high accuracy positioning algorithms to facilitate the use of GNSS or DGNSS for aircraft landing. Firstly, the position error in a typical satellite navigation problem depends on the accuracy of the orbital ephemeris. The thesis presents results for the prediction of the orbital ephemeris from a customised navigation satellite receiver's data message. The SDP4/SDP8 algorithms and suitable noise models are used to establish the measured data. Secondly, the differential station common mode position error not including the contribution due to errors in the ephemeris is usually estimated by employing an EKF. The thesis then considers the application of the UKF to the mixing problem, so as to facilitate the mixing of measurements made by either a GNSS or a DGNSS and a variety of low cost or high-precision INS sensors. Precise, adaptive UKFs and a suitable nonlinear propagation method are used to estimate the orbit ephemeris and the differential position and the navigation filter mixing errors. The results indicate the method is particularly suitable for estimating the orbit ephemeris of navigation satellites and the differential position and navigation filter mixing errors, thus facilitating interoperable DGNSS operation for aircraft landing.
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Weiss, S. "On adaptive filtering in oversampled subbands." Thesis, University of Strathclyde, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.561417.
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For a number of applications like acoustic echo cancellation, adaptive filters are required to identify very long impulse responses. To reduce the computational cost in implementations, adaptive filtering in subband is known to be beneficial. Based on a review of popular fullband adaptive filtering algorithms and various subband approaches, this thesis investigates the implementation, design, and limitations of oversampled subband adaptive filter systems based on modulated complex and real valued filter banks. The main aim is to achieve a computationally efficient implementation for adaptive filter systems, for which fast methods of performing both the subband decomposition and the subband processing are researched. Therefore, a highly efficient polyphase implementation of a complex valued modulated generalized DFT (GDFT) filter bank with a judicious selection of properties for non-integer oversampling ratios is introduced. By modification, a real valued single sideband modulated filter bank is derived. Non-integer oversampling ratios are particularly important when addressing the efficiency of the subband processing. Analysis is presented to decide in which cases it is more advantageous to perform real or complex valued subband processing. Additionally, methods to adaptively adjust the filter lengths in subband adaptive filter (SAF) systems are discussed. Convergence limits for SAFs and the accuracy of the achievable equivalent fullband model based on aliasing and other distortions introduced by the employed filter banks are explicitly derived. Both an approximation of the minimum mean square error and the model accuracy can be directly linked to criteria in the design of the prototype filter for the filter bank. Together with an iterative least-squares design algorithm, it is therefore possible to construct filter banks for SAF applications with pre-defined performance limits. Simulation results are presented which demonstrate the validity and properties of the discussed SAF methods and their advantage over fullband and critically sampled SAF systems.
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Jelfs, Beth. "Collaborative adaptive filtering for machine learning." Thesis, Imperial College London, 2009. http://hdl.handle.net/10044/1/5598.
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Quantitative performance criteria for the analysis of machine learning architectures and algorithms have long been established. However, qualitative performance criteria, which identify fundamental signal properties and ensure any processing preserves the desired properties, are still emerging. In many cases, whilst offline statistical tests exist such as assessment of nonlinearity or stochasticity, online tests which not only characterise but also track changes in the nature of the signal are lacking. To that end, by employing recent developments in signal characterisation, criteria are derived for the assessment of the changes in the nature of the processed signal. Through the fusion of the outputs of adaptive filters a single collaborative hybrid filter is produced. By tracking the dynamics of the mixing parameter of this filter, rather than the actual filter performance, a clear indication as to the current nature of the signal is given. Implementations of the proposed method show that it is possible to quantify the degree of nonlinearity within both real- and complex-valued data. This is then extended (in the real domain) from dealing with nonlinearity in general, to a more specific example, namely sparsity. Extensions of adaptive filters from the real to the complex domain are non-trivial and the differences between the statistics in the real and complex domains need to be taken into account. In terms of signal characteristics, nonlinearity can be both split- and fully-complex and complex-valued data can be considered circular or noncircular. Furthermore, by combining the information obtained from hybrid filters of different natures it is possible to use this method to gain a more complete understanding of the nature of the nonlinearity within a signal. This also paves the way for building multidimensional feature spaces and their application in data/information fusion. To produce online tests for sparsity, adaptive filters for sparse environments are investigated and a unifying framework for the derivation of proportionate normalised least mean square (PNLMS) algorithms is presented. This is then extended to derive variants with an adaptive step-size. In order to create an online test for noncircularity, a study of widely linear autoregressive modelling is presented, from which a proof of the convergence of the test for noncircularity can be given. Applications of this method are illustrated on examples such as biomedical signals, speech and wind data.
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Hawes, Anthony H. "Least squares and adaptive multirate filtering /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03sep%5FHawes.pdf.
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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2003.Thesis advisor(s): Charles W. Therrien, Roberto Cristi. Includes bibliographical references (p. 45). Also available online.
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Sridharan, M. K. "Subband Adaptive Filtering Algorithms And Applications." Thesis, Indian Institute of Science, 2000. https://etd.iisc.ac.in/handle/2005/266.
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In system identification scenario, the linear approximation of the system modelled by its impulse response, is estimated in real time by gradient type Least Mean Square (LMS) or Recursive Least Squares (RLS) algorithms. In recent applications like acoustic echo cancellation, the order of the impulse response to be estimated is very high, and these traditional approaches are inefficient and real time implementation becomes difficult. Alternatively, the system is modelled by a set of shorter adaptive filters operating in parallel on subsampled signals. This approach, referred to as subband adaptive filtering, is expected to reduce not only the computational complexity but also to improve the convergence rate of the adaptive algorithm. But in practice, different subband adaptive algorithms have to be used to enhance the performance with respect to complexity, convergence rate and processing delay. A single subband adaptive filtering algorithm which outperforms the full band scheme in all applications is yet to be realized.
This thesis is intended to study the subband adaptive filtering techniques and explore the possibilities of better algorithms for performance improvement. Three different subband adaptive algorithms have been proposed and their performance have been verified through simulations. These algorithms have been applied to acoustic echo cancellation and EEG artefact minimization problems.
Details of the work
To start with, the fast FIR filtering scheme introduced by Mou and Duhamel has been generalized. The Perfect Reconstruction Filter Bank (PRFB) is used to model the linear FIR system. The structure offers efficient implementation with reduced arithmetic complexity. By using a PRFB with non adjacent filters non overlapping, many channel filters can be eliminated from the structure. This helps in reducing the complexity of the structure further, but introduces approximation in the model. The modelling error depends on the stop band attenuation of the filters of the PRFB. The error introduced due to approximation is tolerable
for applications like acoustic echo cancellation.
The filtered output of the modified generalized fast filtering structure is given by
(formula)
where, Pk(z) is the main channel output, Pk,, k+1 (z) is the output of auxiliary channel filters at the reduced rate, Gk (z) is the kth synthesis filter and M the number of channels in the PRFB. An adaptation scheme is developed for adapting the main channel filters. Auxiliary channel filters are derived from main channel filters.
Secondly, the aliasing problem of the classical structure is reduced without using the cross filters. Aliasing components in the estimated signal results in very poor steady state performance in the classical structure. Attempts to eliminate the aliasing have reduced the computation gain margin and the convergence rate. Any attempt to estimate the subband reference signals from the aliased subband input signals results in aliasing. The analysis filter Hk(z) having the following antialiasing property
(formula)
can avoid aliasing in the input subband signal. The asymmetry of the frequency response prevents the use of real analysis filters. In the investigation presented in this thesis, complex analysis filters and real'synthesis filters are used in the classical structure, to reduce the aliasing errors and to achieve superior convergence rate.
PRFB is traditionally used in implementing Interpolated FIR (IFIR) structure. These filters may not be ideal for processing an input signal for an adaptive algorithm. As third contribution, the IFIR structure is modified using discrete finite frames. The model of an FIR filter s is given by Fc, with c = Hs. The columns of the matrix F forms a frame with rows of H as its dual frame. The matrix elements can be arbitrary except that the transformation should be implementable as a filter bank. This freedom is used to optimize the filter bank, with the knowledge of the input statistics, for initial convergence rate enhancement .
Next, the proposed subband adaptive algorithms are applied to acoustic echo cancellation problem with realistic parameters. Speech input and sufficiently long Room Impulse Response (RIR) are used in the simulations. The Echo Return Loss Enhancement (ERLE)and the steady state error spectrum are used as performance measures to compare these algorithms with the full band scheme and other representative subband implementations.
Finally, Subband adaptive algorithm is used in minimization of EOG (Electrooculogram) artefacts from measured EEG (Electroencephalogram) signal. An IIR filterbank providing sufficient isolation between the frequency bands is used in the modified IFIR structure and this structure has been employed in the artefact minimization scheme. The estimation error in the high frequency range has been reduced and the output signal to noise ratio has been increased by a couple of dB over that of the fullband scheme.
Conclusions
Efforts to find elegant Subband adaptive filtering algorithms will continue in the future. However, in this thesis, the generalized filtering algorithm could offer gain in filtering complexity of the order of M/2 and reduced misadjustment . The complex classical scheme offered improved convergence rate, reduced misadjustment and computational gains of the order of M/4 . The modifications of the IFIR structure using discrete finite frames made it possible to eliminate the processing delay and enhance the convergence rate. Typical performance of the complex classical case for speech input in a realistic scenario (8 channel case), offers ERLE of more than 45dB. The subband approach to EOG artefact minimization in EEG signal was found to be superior to their fullband counterpart.
(Refer PDF file for Formulas)
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Sridharan, M. K. "Subband Adaptive Filtering Algorithms And Applications." Thesis, Indian Institute of Science, 2000. http://hdl.handle.net/2005/266.
Full textAbstract:
In system identification scenario, the linear approximation of the system modelled by its impulse response, is estimated in real time by gradient type Least Mean Square (LMS) or Recursive Least Squares (RLS) algorithms. In recent applications like acoustic echo cancellation, the order of the impulse response to be estimated is very high, and these traditional approaches are inefficient and real time implementation becomes difficult. Alternatively, the system is modelled by a set of shorter adaptive filters operating in parallel on subsampled signals. This approach, referred to as subband adaptive filtering, is expected to reduce not only the computational complexity but also to improve the convergence rate of the adaptive algorithm. But in practice, different subband adaptive algorithms have to be used to enhance the performance with respect to complexity, convergence rate and processing delay. A single subband adaptive filtering algorithm which outperforms the full band scheme in all applications is yet to be realized.
This thesis is intended to study the subband adaptive filtering techniques and explore the possibilities of better algorithms for performance improvement. Three different subband adaptive algorithms have been proposed and their performance have been verified through simulations. These algorithms have been applied to acoustic echo cancellation and EEG artefact minimization problems.
Details of the work
To start with, the fast FIR filtering scheme introduced by Mou and Duhamel has been generalized. The Perfect Reconstruction Filter Bank (PRFB) is used to model the linear FIR system. The structure offers efficient implementation with reduced arithmetic complexity. By using a PRFB with non adjacent filters non overlapping, many channel filters can be eliminated from the structure. This helps in reducing the complexity of the structure further, but introduces approximation in the model. The modelling error depends on the stop band attenuation of the filters of the PRFB. The error introduced due to approximation is tolerable
for applications like acoustic echo cancellation.
The filtered output of the modified generalized fast filtering structure is given by
(formula)
where, Pk(z) is the main channel output, Pk,, k+1 (z) is the output of auxiliary channel filters at the reduced rate, Gk (z) is the kth synthesis filter and M the number of channels in the PRFB. An adaptation scheme is developed for adapting the main channel filters. Auxiliary channel filters are derived from main channel filters.
Secondly, the aliasing problem of the classical structure is reduced without using the cross filters. Aliasing components in the estimated signal results in very poor steady state performance in the classical structure. Attempts to eliminate the aliasing have reduced the computation gain margin and the convergence rate. Any attempt to estimate the subband reference signals from the aliased subband input signals results in aliasing. The analysis filter Hk(z) having the following antialiasing property
(formula)
can avoid aliasing in the input subband signal. The asymmetry of the frequency response prevents the use of real analysis filters. In the investigation presented in this thesis, complex analysis filters and real'synthesis filters are used in the classical structure, to reduce the aliasing errors and to achieve superior convergence rate.
PRFB is traditionally used in implementing Interpolated FIR (IFIR) structure. These filters may not be ideal for processing an input signal for an adaptive algorithm. As third contribution, the IFIR structure is modified using discrete finite frames. The model of an FIR filter s is given by Fc, with c = Hs. The columns of the matrix F forms a frame with rows of H as its dual frame. The matrix elements can be arbitrary except that the transformation should be implementable as a filter bank. This freedom is used to optimize the filter bank, with the knowledge of the input statistics, for initial convergence rate enhancement .
Next, the proposed subband adaptive algorithms are applied to acoustic echo cancellation problem with realistic parameters. Speech input and sufficiently long Room Impulse Response (RIR) are used in the simulations. The Echo Return Loss Enhancement (ERLE)and the steady state error spectrum are used as performance measures to compare these algorithms with the full band scheme and other representative subband implementations.
Finally, Subband adaptive algorithm is used in minimization of EOG (Electrooculogram) artefacts from measured EEG (Electroencephalogram) signal. An IIR filterbank providing sufficient isolation between the frequency bands is used in the modified IFIR structure and this structure has been employed in the artefact minimization scheme. The estimation error in the high frequency range has been reduced and the output signal to noise ratio has been increased by a couple of dB over that of the fullband scheme.
Conclusions
Efforts to find elegant Subband adaptive filtering algorithms will continue in the future. However, in this thesis, the generalized filtering algorithm could offer gain in filtering complexity of the order of M/2 and reduced misadjustment . The complex classical scheme offered improved convergence rate, reduced misadjustment and computational gains of the order of M/4 . The modifications of the IFIR structure using discrete finite frames made it possible to eliminate the processing delay and enhance the convergence rate. Typical performance of the complex classical case for speech input in a realistic scenario (8 channel case), offers ERLE of more than 45dB. The subband approach to EOG artefact minimization in EEG signal was found to be superior to their fullband counterpart.
(Refer PDF file for Formulas)
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Hutchinson, James H. "Reduced-order adaptive control." Thesis, This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-05022009-040532/.
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Zhang, Jie. "Blind adaptive cyclic filtering and beamforming algorithms /." *McMaster only, 2001.
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Torgrimsson, Jan. "Adaptive filtering of VLF data from space." Thesis, KTH, Rymd- och plasmafysik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91544.
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Mayyass, Khaled A. "Gradient adaptive digital filtering: Problems and solutions." Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/9498.
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The LMS adaptive algorithm has always been attractive to researchers in the field of adaptive signal processing due to its inherent conceptual and implementational simplicity. Unfortunately, this elegant simplicity is undermined by problems associated with the direct use of the LMS algorithm. One of the main disadvantages of the LMS is its relatively slow convergence. We deal with this problem for FIR adaptive filters by proposing two algorithms based on different approaches. The first algorithm relies on the time-varying step size approach. The step size of the algorithm is adjusted according to an error autocorrelation function. As a result, the algorithm can efficiently sense the adaptation state while maintaining the immunity against independent noise disturbance. The second algorithm is a gradient-based one that combines time- and order-updating when searching the bottom of the MSE surface, thus resulting in more efficient use of the available information. Moreover, two possibilities for the order update are considered: straightforward sequential or selective schemes. Approximate analysis of convergence and steady state performance of the two algorithms are provided. The slow convergence problem of the LMS algorithm is also investigated for IIR adaptive filters based on output-error formulation. A new adaptive algorithm is proposed. The algorithm combines the least mean square (LMS) method with its low complexity and the least squares method with its fast convergence into a coupled LMS-LS adaptive scheme. Simulation examples indicate that the proposed scheme converges significantly faster than the LMS with minimal increase in complexity. Next, we consider the Leaky LMS algorithm as an LMS variant proposed to deal with numerous problems that arise in direct application of LMS, including: lack of persistent excitation in the input sequence, stalling, bursting, etc. However, despite the wide spread usage of the Leaky LMS, there has been no detailed study of its performance. We present an analytical treatment of the mean square error for zero-mean Gaussian input data. Exact expressions for the second moment of the coefficient vector, the algorithm misadjustment, and rigorous conditions for MSE convergence are derived. Finally, we consider one of the common applications of the LMS algorithm, echo cancellation in telephone networks. We investigate the presence of bursting on a back-to-back hybrid connection. Based on the essential fact that the high cross correlation between the input to the adaptive echo canceler and the transmitted signal at the near-end is the root cause of the bursting problem, we modify the conventional echo canceler such that under bursting circumstances the cross-correlation is substantially reduced and bursting is averted. The proposed system ensures normal operation is not affected. Implementation details of the proposed system are studied.
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Khan, Imran. "Personal adaptive web agent for information filtering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq23361.pdf.
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Marath, Ajitha T. "Adaptive user modeling for filtering electronic news." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ57309.pdf.
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Papoulis, Eftychios. "Structures and algorithms for subband adaptive filtering." Thesis, Imperial College London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.429497.
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Fee, D. T. "Dereverberation of acoustic signals via adaptive filtering." Thesis, Queen's University Belfast, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.438629.
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Talebi, Sayedpouria. "Adaptive filtering algorithms for quaternion-valued signals." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/44568.
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Advances in sensor technology have made possible the recoding of three and four-dimensional signals which afford a better representation of our actual three-dimensional world than the ''flat view'' one and two-dimensional approaches. Although it is straightforward to model such signals as real-valued vectors, many applications require unambiguous modeling of orientation and rotation, where the division algebra of quaternions provides crucial advantages over real-valued vector approaches. The focus of this thesis is on the use of recent advances in quaternion-valued signal processing, such as the quaternion augmented statistics, widely-linear modeling, and the HR-calculus, in order to develop practical adaptive signal processing algorithms in the quaternion domain which deal with the notion of phase and frequency in a compact and physically meaningful way. To this end, first a real-time tracker of quaternion impropriety is developed, which allows for choosing between strictly linear and widely-linear quaternion-valued signal processing algorithms in real-time, in order to reduce computational complexity where appropriate. This is followed by the strictly linear and widely-linear quaternion least mean phase algorithms that are developed for phase-only estimation in the quaternion domain, which is accompanied by both quantitative performance assessment and physical interpretation of operations. Next, the practical application of state space modeling of three-phase power signals in smart grid management and control systems is considered, and a robust complex-valued state space model for frequency estimation in three-phase systems is presented. Its advantages over other available estimators are demonstrated both in an analytical sense and through simulations. The concept is then expanded to the quaternion setting in order to make possible the simultaneous estimation of the system frequency and its voltage phasors. Furthermore, a distributed quaternion Kalman filtering algorithm is developed for frequency estimation over power distribution networks and collaborative target tracking. Finally, statistics of stable quaternion-valued random variables, that include quaternion-valued Gaussian random variables as a special case, is investigated in order to develop a framework for the modeling and processing of heavy-tailed quaternion-valued signals.
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Maloney, Thomas C. "Adaptive Array-Gain Spatial Filtering in Magnetoencephalography." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1273001694.
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Sathe, Vinay Padmakar Vaidyanathan P. P. Vaidyanathan P. P. "Multirate adaptive filtering algorithms : analysis and applications /." Diss., Pasadena, Calif. : California Institute of Technology, 1991. http://resolver.caltech.edu/CaltechETD:etd-07122007-103754.
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Yang, Jia-Horng. "Robust adaptive control using a filtering action." Monterey, California : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/dissert/2009/Sep/09Sep_Yang_PhD.pdf.
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Dissertation (Ph.D. in Electrical Engineering)--Naval Postgraduate School, September 2009.Dissertation Advisor(s): Cristi, Roberto. "September 2009." Description based on title screen as viewed on November 6, 2009. Author(s) subject terms: low pass filter, L1 adaptive controller, unmodeled dynamics, non-minimum phase, PID feedback, flexible problems. Includes bibliographical references (p. 95-102). Also available in print.
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Oddiraju, Swetha. "Improving performance for adaptive filtering with voice applications." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/6271.
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Thesis (M.S.)--University of Missouri-Columbia, 2007.The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on September 29, 2008) Includes bibliographical references.
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Almosallam, Ibrahim Ahmad Shang Yi. "A new adaptive framework for collaborative filtering prediction." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5630.
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Thesis (M.S.)--University of Missouri-Columbia, 2008.The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 22, 2008) Includes bibliographical references.
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Andersson, Mats, and Hans Knutsson. "Adaptive Spatio-temporal Filtering of 4D CT-Heart." Linköpings universitet, Medicinsk informatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-92725.
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The aim of this project is to keep the x-ray exposure of the patient as low as reasonably achievable while improving the diagnostic image quality for the radiologist. The means to achieve these goals is to develop and evaluate an ecient adaptive ltering (denoising/image enhancement) method that fully explores true 4D image acquisition modes. The proposed prototype system uses a novel lter set having directional lter responses being monomials. The monomial lter concept is used both for estimation of local structure and for the anisotropic adaptive ltering. Initial tests on clinical 4D CT-heart data with ECG-gated exposure has resulted in a signicant reduction of the noise level and an increased detail compared to 2D and 3D methods. Another promising feature is that the reconstruction induced streak artifacts which generally occur in low dose CT are remarkably reduced in 4D.
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Ramachandran, Ravi P. "Pitch filtering in adaptive predictive coding of speech." Thesis, McGill University, 1986. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=65345.
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Boudreau, Daniel. "Joint time delay estimation and adaptive filtering techniques." Thesis, McGill University, 1990. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=70177.
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This thesis studies adaptive filters for the case in which the main input signal is not synchronized with the reference signal. The asynchrony is modelled by a time-varying delay. This delay has to be estimated and compensated. This is accomplished by designing and investigating joint delay estimation and adaptive filtering algorithms. First, joint maximum likelihood estimator is derived for input Gaussian signals. It is used to define a readily implementable joint estimator, composed of an adaptive delay element and an adaptive filter. Next, two estimation criteria are investigated with that structure. The minimum mean squared error criterion is used with a joint steepest-descent adaptive algorithm and with a joint least-mean-square adaptive algorithm. The general convergence conditions of the joint steepest-descent algorithm are derived. The joint LMS algorithm is analysed in terms of joint convergence in the mean and in the mean square. Finally, a joint recursive least squares adaptive algorithm is investigated in conjunction with the exponentially weighted least squares criterion. Experimental results are obtained for these different adaptive algorithms, in order to verify the analyses. The results show that the joint algorithms improve the performance of the conventional adaptive filtering techniques.
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Oakman, Amere. "Dynamic non-uniform filterbanks for subband adaptive filtering." Thesis, Imperial College London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406180.
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Kim, Dai Il. "Transform layered stochastic gradient-type adaptive filtering structures." Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.394382.
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Jaffer, Sadiq. "Noise adaptive particle filtering for mobile robot applications." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/34557/.
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Kabbara, Jad. "Kernel adaptive filtering algorithms with improved tracking ability." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=123272.
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In recent years, there has been an increasing interest in kernel methods in areas such as machine learning and signal processing as these methods show strong performance in classification and regression problems. Interesting "kernelized" extensions of many well-known algorithms in artificial intelligence and signal processing have been presented, particularly, kernel versions of the popular online recursive least squares (RLS) adaptive algorithm, namely kernel RLS (KRLS). These algorithms have been receiving significant attention over the past decade in statistical estimation problems, among which those problems involving tracking time-varying systems. KRLS algorithms obtain a non-linear least squares (LS) regressor as a linear combination of kernel functions evaluated at the elements of a carefully chosen subset, called a dictionary, of the received input vectors. As such, the number of coefficients in that linear combination, i.e., the weights, is equal to the size of the dictionary. This coupling between the number of weights and the dictionary size introduces a trade-off. On one hand, a large dictionary would accurately capture the dynamics of the input-output relationship over time. On the other, it has a detrimental effect on the algorithm's ability to track changes in that relationship because having to adjust a large number of weights can significantly slow down adaptation. In this thesis, we present a new KRLS algorithm designed specifically for the tracking of time-varying systems. The key idea behind the proposed algorithm is to break the dependency of the number of weights on the dictionary size. In the proposed method, the number of weights K is fixed and is independent from the dictionary size.Particularly, we use a novel hybrid approach for the construction of the dictionary that employs the so-called surprise criterion for admitting data samples along with a simple pruning method ("remove-the-oldest") that imposes a hard limit on the dictionary size. Then, we propose to construct a K-sparse LS regressor tracking the relationship of the most recent training input-output pairs using the K dictionary elements that provide the best approximation of the output values. Identifying those dictionary elements is a combinatorial optimization problem with a prohibitive computational complexity. To overcome this, we extend the Subspace Pursuit algorithm (SP) which, in essence, is a low complexity method to obtain LS solutions with a pre-specified sparsity level, to non-linear regression problems and introduce a kernel version of SP, which we call Kernel SP (KSP). The standard KRLS is used to recursively update the weights until a new dictionary element selection is triggered by the admission of a new input vector to the dictionary. Simulations show that that the proposed algorithm outperforms existing KRLS-type algorithms in tracking time-varying systems and highly chaotic time series.Au cours des dernières années, il y a eu un intérêt accru pour les méthodes à noyau dans des domaines tels que l'apprentissage automatique et le traitement du signal, puisque ces méthodes démontrent une performance supérieure dans la résolution des problèmes de classification et de régression. D'intéressantes extensions à noyau de plusieurs algorithmes connus en intelligence artificielle et en traitement du signal ont été introduites, particulièrement, les versions à noyau du fameux algorithme d'apprentissage incrémental des moindres carrés récursifs (en anglais, Recursive Least Squares (RLS)), nommées KRLS. Ces algorithmes ont reçu une attention considérable durant la dernière décennie dans les problèmes d'estimation statistique, particulièrement ceux de suivi des systèmes variant dans le temps. Les algorithmes KRLS forment le régresseur aux moindres carrés non-linéaires en utilisant une combinaison linéaire de noyaux évalués aux membres d'un sous-ensemble, appelé dictionnaire, des données d'entrée. Le nombre des coefficients dans la combinaison linéaire, c'est à dire les poids, est égal à la taille du dictionnaire. Ce couplage entre le nombre de poids et la taille du dictionnaire introduit un compromis. D'une part, un dictionnaire de grande taille reflète avec précision la dynamique de la relation entre les données d'entrée et les sorties à travers le temps. De l'autre part, un tel dictionnaire diminue la capacité de l'algorithme à suivre les variations dans cette relation, car ajuster un grand nombre de poids ralentit considérablement l'adaptation de l'algorithme aux variations du système. Dans cette thèse, nous présentons un nouvel algorithme KRLS conçu précisément pour suivre les systèmes variant dans le temps. L'idée principale de l'algorithme est d'enlever la dépendance du nombre de poids sur la taille du dictionnaire. Ainsi, nous proposons de fixer le nombre de poids indépendamment de la taille du dictionnaire.Particulièrement, nous présentons une nouvelle approche hybride pour la construction du dictionnaire qui emploie le test de la surprise pour l'admission des données d'entrées avec une méthode simple d'élagage (l'élimination du membre le plus ancien du dictionnaire) qui impose une limite stricte sur la taille du dictionnaire. Nous proposons ainsi de construire un régresseur "K-creux" (en anglais, K-sparse) aux moindres carrés qui suit la relation des paires de données d'entrées et sorties les plus récentes en utilisant les K membres du dictionnaire qui approximent le mieux possible les sorties. L'identification de ces membres est un problème d'optimisation combinatoire ayant une complexité prohibitive. Pour surmonter cet obstacle, nous étendons l'algorithme Subspace Pursuit (SP), qui est une méthode à complexité réduite pour le calcul des solutions aux moindres carrés ayant un niveau préfixé de parcimonie, aux problèmes de régression non-linéaire. Ainsi, nous introduisons une version à noyau de SP qu'on appelle Kernel Subspace Pursuit (KSP). L'algorithme standard KRLS est utilisé pour l'ajustement récursif des poids jusqu'à ce qu'un nouveau vecteur de donnée soit admis au dictionnaire. Les simulations démontrent que la performance de notre algorithme dans le cadre du suivi des systèmes variant dans le temps surpasse celle d'autres algorithmes KRLS.
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Tam, Pik Shan. "Constrained adaptive filtering and application to sound equalisation." Thesis, University of Southampton, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398604.
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Ludwig, Jeffrey Thomas 1968. "Low power digital filtering using adaptive approximate processing." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/42766.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 167-173).
by Jeffrey Thomas Ludwig.
Ph.D.
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Lampl, Tanja. "Implementation of adaptive filtering algorithms for noise cancellation." Thesis, Högskolan i Gävle, Avdelningen för elektroteknik, matematik och naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-33277.
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This paper deals with the implementation and performance evaluation of adaptive filtering algorithms for noise cancellation without reference signal. Noise cancellation is a technique of estimating a desired signal from a noise-corrupted observation. If the signal and noise characteristics are unknown or change continuously over time, the need of adaptive filter arises. In contrast to the conventional digital filter design techniques, adaptive filters do not have constant filter parameters, they have the capability to continuously adjust their coefficients to their operating environment. To design an adaptive filter, that produces an optimum estimate of the desired signal from the noisy environment, different adaptive filtering algorithms are implemented and compared to each other. The Least Mean Square LMS, the Normalized Least Mean Square NLMS and the Recursive Least Square RLS algorithm are investigated. Three performance criteria are used in the study of these algorithms: the rate of convergence, the error performance and the signal-to-noise ratio SNR. The implementation results show that the adaptive noise cancellation application benefits more from the use of the NLMS algorithm instead of the LMS or RLS algorithm.
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Burns, Clinton Wyatt. "Development Towards the use of Beamforming and Adaptive Line Enhancers for Audio Detection of Quadcopters." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/84522.
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The usage of Unmanned Aerial Systems (UASs), such as quadcopters and hexacopters, has steadily increased over the past few years in both recreational and commercial use. This increased availability to purchase such systems has also given rise to many safety and security concerns. A common concern is that the misuse of a UAS can cause damage to airplanes and helicopters in and around airports. Another growing concern is the use of UASs for terrorist intentions such as using the UAS as a remote controlled bomb. There is clearly a need to be able to detect the presence of unwanted UASs in restricted areas. This thesis work presents the beginning work towards a method to detect the presence of these UASs using the blade pass frequency (BPF) of the motors and rotors of a home made quadcopter. A low cost uniform linear microphone array is first used to perform a simple delay-and-sum beamformer to spatially filter out noise sources. The beamformer output is then divided into sub-bands using three bandpass filters centered on the expected location of the fundamental BPF and its 2nd and 3rd harmonics. For each sub-band, an adaptive filter called an adaptive line enhancer is used to extract and enhance the narrowband signals. The response of the adaptive filters are then used to detect the quadcopter by looking for the presence of the 2nd and 3rd harmonics of the fundamental BPF. Static tests of the quadcopter out in a field showed promising results for this method with the ability to detect up to the 3rd harmonic 90ft away and the 2nd harmonic 130 ft away.Master of Science
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Birkett, A. Neil. "Nonlinear adaptive filtering with application to acoustic echo cancellation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ26845.pdf.
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Adamo, Ronald C. "Adaptive windows via Kalman filtering in the spectral domain." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/27934.
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Silva, Rodrigo Cardoso da. "Filtering and adaptive control for balancing a nanosatellite testbed." reponame:Repositório Institucional da UnB, 2018. http://repositorio.unb.br/handle/10482/34210.
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Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2018.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) e Fundação de Apoio à Pesquisa do Distrito Federal (FAPDF).
O Laboratório de Aplicação e Inovação em Ciências Aeroespaciais (LAICA) da Universidade de Brasília (UnB) está desenvolvendo uma plataforma de testes de nanossatélites capaz de simular condições ambientais vistas no espaço, especialmente no que diz respeito ao campo magnético da Terra em órbitas, o movimento rotational livre de atrito e o torque gravitacional baixo. Essa plataforma compreende vários subsistemas, tais como uma mesa com rolamento a ar, na qual nanossatélites são montados para teste de seus subsistemas; uma gaiola de Helmholtz, responsável por simular o campo magnético da Terra presente em vários tipos de órbita, especialmente órbitas de baixa altitude (LOE), que são as mais comuns para nanossatélites; sistemas de atuação, tais como rodas de reação e atuadores magnéticos, usados para estudar estratégias de controle de atitude, e sistemas de determinação de atitude, tais como aqueles baseados em telemetria embarcada ou visão computacional. A mesa com rolamento a ar é a parte responsável por fornecer o movimento livre de atrito com três graus de liberdade rotacionais. Ademais, para fornecer o requisito de torque gravitacional baixo, um método deve ser desenvolvido para balancear a mesa com rolamento a ar. Neste trabalho, foco é dado para a solução desse problema. Vários métodos para balanceamento da plataforma de testes do LAICA são apresentados, especialmente quanto às soluções de filtragem, como aquelas que utilizam o filtro de Kalman e suas variações, e esquemas de controle adaptativo, auxiliados pela teoria de Lyapunov. A performance dos métodos de balanceamento propostos é avaliada por meio de simulações e experimentos.
The Laboratory of Application and Innovation in Aerospace Science (LAICA) of the University of Brasília (UnB) is developing a nanosatellite testbed capable of simulating the environment conditions seen in space, specially regarding the Earth magnetic field in orbits, the frictionless rotational movement and the low gravitational torque. This testbed comprises various subsystems, such as an air bearing table, on which nanosatellites are mounted for testing its subsystems; a Helmholtz cage, responsible for simulating the Earth magnetic field present in various kinds of orbit, specially Low Earth Orbits, which is the most common for nanosatellites; actuation systems, such as reaction wheels and magnetorquers, used to study attitude control strategies, and attitude determination systems, such as those based on embedded telemetry or computer vision. The air bearing table is the part responsible for providing the frictionless movement with three rotational degrees of freedom. Also, for providing the low gravitational torque requisite, a method must be developed for balancing the air bearing table. In this work, focus is given for solving this problem. Various methods for balancing the LAICA testbed are presented, specially regarding filtering solutions, such as those using the Kalman Filter and its variations, and adaptive control schemes, aided by the Lyapunov theory. The performance of the proposed balancing methods is evaluated through simulations and experiments.
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Goertz, Michael Brian 1978. "A reduced complexity adaptive filtering system for directional listening." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86712.
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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (leaves 71-72).
by Michael Brian Goertz.
M.Eng.
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Nadakuditi, Rajesh Rao. "A channel subspace post-filtering approach to adaptive equalization." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87613.
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Thesis (S.M.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2002.Includes bibliographical references (p. 151-154).
by Rajesh Rao Naduditi.
S.M.
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Kapanipathi, Pavan. "Personalized and Adaptive Semantic Information Filtering for Social Media." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464541093.
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