Visible to the public Adaptive Filtering

SoS Newsletter- Advanced Book Block

Adaptive Filtering

As the power of digital signal processors has increased, adaptive filters are now routinely used in many devices as varied as mobile phones, printers, cameras, power systems, GPS devices and medical monitoring equipment. An adaptive filter uses an optimization algorithm is a system with a linear filter to adjust parameters that have a transfer function controlled by variable parameter. Because of the complexity of the optimization algorithms, most of these adaptive filters are digital filters. They are required for some applications because some parameters of the desired processing operation are not known in advance or are changing. The articles below were published from January through August, 2014.

  • Markman, A; Javidi, B.; Tehranipoor, M., "Photon-Counting Security Tagging and Verification Using Optically Encoded QR Codes," Photonics Journal, IEEE, vol.6, no.1, pp.1,9, Feb. 2014. We propose an optical security method for object authentication using photon-counting encryption implemented with phase encoded QR codes. By combining the full phase double-random-phase encryption with photon-counting imaging method and applying an iterative Huffman coding technique, we are able to encrypt and compress an image containing primary information about the object. This data can then be stored inside of an optically phase encoded QR code for robust read out, decryption, and authentication. The optically encoded QR code is verified by examining the speckle signature of the optical masks using statistical analysis. Optical experimental results are presented to demonstrate the performance of the system. In addition, experiments with a commercial Smartphone to read the optically encoded QR code are presented. To the best of our knowledge, this is the first report on integrating photon-counting security with optically phase encoded QR codes.
    Keywords: Huffman codes; cryptography; image coding; iterative methods; masks; phase coding;p hoton counting; smart phones; speckle; statistical analysis; commercial Smartphone; decryption; full phase double-random-phase encryption image compressing; image encryption; iterative Huffman coding; object authentication; optical masks; optical security method; optically phase encoded QR code; photon-counting encryption; photon-counting imaging; photon-counting security tagging; robust read out; speckle signature; statistical analysis; Adaptive optics; Cryptography; Nonlinear optics; Optical filters; Optical imaging; Optical polarization; Photonics; Optical security and encryption; coherent imaging; photon counting imaging; speckle (ID#:14-2028)
  • Tsilopoulos, C.; Xylomenos, G.; Thomas, Y., "Reducing forwarding state in content-centric networks with semi-stateless forwarding," INFOCOM, 2014 Proceedings IEEE , vol., no., pp.2067,2075, April 27 2014-May 2 2014. Routers in the Content-Centric Networking (CCN) architecture maintain state for all pending content requests, so as to be able to later return the corresponding content. By employing stateful forwarding, CCN supports native multicast, enhances security and enables adaptive forwarding, at the cost of excessive forwarding state that raises scalability concerns. We propose a semi-stateless forwarding scheme in which, instead of tracking each request at every on-path router, requests are tracked at every d hops. At intermediate hops, requests gather reverse path information, which is later used to deliver responses between routers using Bloom filter-based stateless forwarding. Our approach effectively reduces forwarding state, while preserving the advantages of CCN forwarding. Evaluation results over realistic ISP topologies show that our approach reduces forwarding state by 54%-70% in unicast delivery, without any bandwidth penalties, while in multicast delivery it reduces forwarding state by 34%-55% at the expense of 6%-13% in bandwidth overhead.
    Keywords: computer networks; data structures ;topology; Bloom filter-based stateless forwarding; CCN architecture; ISP topologies; adaptive forwarding; content-centric networking architecture; semi-stateless forwarding scheme; unicast delivery;Bandwidth; Computer architecture ;Computers; Conferences; Ports (Computers); Probabilistic logic; Unicast (ID#:14-2029)
  • Boruah, A; Hazarika, S.M., "An MEBN framework as a dynamic firewall's knowledge flow architecture," Signal Processing and Integrated Networks (SPIN), 2014 International Conference on , vol., no., pp.249,254, 20-21 Feb. 2014. Dynamic firewalls with stateful inspection have added a lot of security features over the stateless traditional static filters. Dynamic firewalls need to be adaptive. In this paper, we have designed a framework for dynamic firewalls based on probabilistic ontology using Multi Entity Bayesian Networks (MEBN) logic. MEBN extends ordinary Bayesian networks to allow representation of graphical models with repeated substructures and can express a probability distribution over models of any consistent first order theory. The motivation of our proposed work is about preventing novel attacks (i.e. those attacks for which no signatures have been generated yet). The proposed framework is in two important parts: first part is the data flow architecture which extracts important connection based features with the prime goal of an explicit rule inclusion into the rule base of the firewall; second part is the knowledge flow architecture which uses semantic threat graph as well as reasoning under uncertainty to fulfill the required objective of providing futuristic threat prevention technique in dynamic firewalls.
    Keywords: belief networks; data flow computing; firewalls; ontologies (artificial intelligence); statistical distributions; MEBN framework; MEBN logic; data flow architecture; dynamic firewalls; first order theory; futuristic threat prevention technique; graphical models; knowledge flow architecture; multi entity Bayesian networks; probabilistic ontology; probability distribution; security features; stateful inspection; stateless traditional static filters; Bayes methods ;Feature extraction; Ontologies; Probabilistic logic; Semantics; Signal processing algorithms; Bayesian networks; MEBN; Probabilistic Ontology; explicit rule inclusion; semantic threat graph (ID#:14-2030)
  • Jian Wang; Lin Mei; Yi Li; Jian-Ye Li; Kun Zhao; Yuan Yao, "Variable Window for Outlier Detection and Impulsive Noise Recognition in Range Images," Cluster, Cloud and Grid Computing (CCGrid), 2014 14th IEEE/ACM International Symposium on , vol., no., pp.857,864, 26-29 May 2014. To improve comprehensive performance of denoising range images, an impulsive noise (IN) denoising method with variable windows is proposed in this paper. Founded on several discriminant criteria, the principles of dropout IN detection and outlier IN detection are provided. Subsequently, a nearest non-IN neighbors searching process and an Index Distance Weighted Mean filter is combined for IN denoising. As key factors of adapatablity of the proposed denoising method, the sizes of two windows for outlier INs detection and INs denoising are investigated. Originated from a theoretical model of invader occlusion, variable window is presented for adapting window size to dynamic environment of each point, accompanying with practical criteria of adaptive variable window size determination. Experiments on real range images of multi-line surface are proceeded with evaluations in terms of computational complexity and quality assessment with comparison analysis among a few other popular methods. It is indicated that the proposed method can detect the impulsive noises with high accuracy, meanwhile, denoise them with strong adaptability with the help of variable window.
    Keywords: computational complexity ;image denoising ;image recognition; impulse noise; adaptive variable window size determination; computational complexity; discriminant criteria; dropout IN detection; dynamic environment; impulsive noise denoising; impulsive noise recognition; index distance weighted mean filter; invader occlusion; multiline surface; nearest nonIN neighbors searching process; outlier IN detection; quality assessment; range image denoising; Algorithm design and analysis; Educational institutions; Image denoising; Indexes; Noise; Noise reduction; Wavelet transforms; Impulsive noise recognition; Index Distance Weighted Mean filter; Outlier detection; Range image denoising; Variable window (ID#:14-2031)
  • Weikun Hou; Xianbin Wang; Chouinard, J.-Y.; Refaey, A, "Physical Layer Authentication for Mobile Systems with Time-Varying Carrier Frequency Offsets," Communications, IEEE Transactions on , vol.62, no.5, pp.1658,1667, May 2014. A novel physical layer authentication scheme is proposed in this paper by exploiting the time-varying carrier frequency offset (CFO) associated with each pair of wireless communications devices. In realistic scenarios, radio frequency oscillators in each transmitter-and-receiver pair always present device-dependent biases to the nominal oscillating frequency. The combination of these biases and mobility-induced Doppler shift, characterized as a time-varying CFO, can be used as a radiometric signature for wireless device authentication. In the proposed authentication scheme, the variable CFO values at different communication times are first estimated. Kalman filtering is then employed to predict the current value by tracking the past CFO variation, which is modeled as an autoregressive random process. To achieve the proposed authentication, the current CFO estimate is compared with the Kalman predicted CFO using hypothesis testing to determine whether the signal has followed a consistent CFO pattern. An adaptive CFO variation threshold is derived for device discrimination according to the signal-to-noise ratio and the Kalman prediction error. In addition, a software-defined radio (SDR) based prototype platform has been developed to validate the feasibility of using CFO for authentication. Simulation results further confirm the effectiveness of the proposed scheme in multipath fading channels.
    Keywords: Doppler shift; Kalman filters; fading channels; multipath channels; radio networks; radio receivers; radio transmitters; software radio; telecommunication security; CFO; Doppler shift; Kalman filtering; Kalman prediction error; SDR; mobile systems; multipath fading channels; nominal oscillating frequency; physical layer authentication; prototype platform; radio frequency oscillators; radiometric signature; receiver; signal-to-noise ratio; software defined radio; time varying carrier frequency offsets; transmitter; wireless communications devices; wireless device authentication; Authentication; Doppler shift; Estimation; Kalman filters; Physical layer; Signal to noise ratio; Wireless communication; Kalman filtering; Physical layer authentication; carrier frequency offset (CFO);hypothesis testing (ID#:14-2032)
  • Huang, T.; Drake, B.; Aalfs, D.; Vidakovic, B., "Nonlinear Adaptive Filtering with Dimension Reduction in the Wavelet Domain," Data Compression Conference (DCC), 2014 , vol., no., pp.408,408, 26-28 March 2014. Recent advances in adaptive filter theory and the hardware for signal acquisition have led to the realization that purely linear algorithms are often not adequate in these domains. Nonlinearities in the input space have become apparent with today's real world problems. Algorithms that process the data must keep pace with the advances in signal acquisition. Recently kernel adaptive (online) filtering algorithms have been proposed that make no assumptions regarding the linearity of the input space. Additionally, advances in wavelet data compression/dimension reduction have also led to new algorithms that are appropriate for producing a hybrid nonlinear filtering framework. In this paper we utilize a combination of wavelet dimension reduction and kernel adaptive filtering. We derive algorithms in which the dimension of the data is reduced by a wavelet transform. We follow this by kernel adaptive filtering algorithms on the reduced-domain data to find the appropriate model parameters demonstrating improved minimization of the mean-squared error (MSE). Another important feature of our methods is that the wavelet filter is also chosen based on the data, on-the-fly. In particular, it is shown that by using a few optimal wavelet coefficients from the constructed wavelet filter for both training and testing data sets as the input to the kernel adaptive filter, convergence to the near optimal learning curve (MSE) results. We demonstrate these algorithms on simulated and a real data set from food processing.
    Keywords: adaptive filters; mean square error methods; wavelet transforms; MSE minimization; kernel adaptive filtering; mean-squared error minimization; nonlinear adaptive filtering; wavelet coefficients; wavelet dimension reduction; wavelet domain; wavelet transform; Adaptive filters; Algorithm design and analysis; Kernel; Training; Wavelet domain; Wavelet transforms; Pollen wavelets; dimension reduction; kernel adaptive filtering; wavelet transform (ID#:14-2033)
  • Nikolic, G.; Nikolic, T.; Petrovic, B., "Using Adaptive Filtering In Single-Phase Grid-Connected System," Microelectronics Proceedings - MIEL 2014, 2014 29th International Conference on , vol., no., pp.417,420, 12-14 May 2014. Recently, there has been a pronounced increase of interest in the field of renewable energy. In this area power inverters are crucial building blocks in a segment of energy converters, since they change direct current (DC) to alternating current (AC). Grid connected power inverters should operate in synchronism with the grid voltage. In this paper, the structure of a power system based on adaptive filtering is described. The main purpose of the adaptive filter is to adapt the output signal of the inverter to the corresponding load and/or grid signal. By involving adaptive filtering the response time decreases and quality of power delivery to the load or grid increases. A comparative analysis which relates to power system operation without and with adaptive filtering is given. In addition, the impact of variable impedance of load on quality of delivered power is considered. Results which relates to total harmonic distortion (THD) factor are obtained by Matlab/Simulink software.
    Keywords: adaptive filters; harmonic distortion; invertors; adaptive filtering; alternating current; direct current; energy converters; ower delivery; power inverters; renewable energy;response time; single phase grid connected system; total harmonic distortion factor; variable impedance; Adaptive filters; Adaptive systems; Inverters; Least squares approximations; Power harmonic filters; Pulse width modulation (ID#:14-2034)
  • Zhen Jiang; Shihong Miao; Pei Liu, "A Modified Empirical Mode Decomposition Filtering-Based Adaptive Phasor Estimation Algorithm for Removal of Exponentially Decaying DC Offset," Power Delivery, IEEE Transactions on , vol.29, no.3, pp.1326,1334, June 2014. This paper proposes a modified empirical-mode decomposition (EMD) filtering-based adaptive dynamic phasor estimation algorithm for the removal of exponentially decaying dc offset. Discrete Fourier transform does not have the ability to attain the accurate phasor of the fundamental frequency component in digital protective relays under dynamic system fault conditions because the characteristic of exponentially decaying dc offset is not consistent. EMD is a fully data-driven, not model-based, adaptive filtering procedure for extracting signal components. But the original EMD technique has high computational complexity and requires a large data series. In this paper, a short data series-based EMD filtering procedure is proposed and an optimum hermite polynomial fitting (OHPF) method is used in this modified procedure. The proposed filtering technique has high accuracy and convergent speed, and is greatly appropriate for relay applications. This paper illustrates the characteristics of the proposed technique and evaluates its performance by computer-simulated signals, PSCAD/EMTDC-generated signals, and real power system fault signals.
    Keywords: adaptive filters; discrete Fourier transforms; phasor measurement; polynomial approximation; power harmonic filters; power system faults; relays; adaptive dynamic phasor estimation algorithm; adaptive filtering procedure; digital protective relays; discrete Fourier transform; dynamic system fault; exponentially decaying DC offset; fundamental frequency component; modified empirical mode decomposition filtering; optimum hermite polynomial fitting method; real power system fault signals; Algorithm design and analysis; Discrete Fourier transforms; Estimation; Heuristic algorithms; Polynomials; Power system dynamics; Relays; Adaptive filtering; empirical mode decomposition; exponentially decaying dc offset; optimum hermite polynomial fitting; phasor estimation (ID#:14-2035)
  • Bhotto, M.Z.A; Antoniou, A, "Affine-Projection-Like Adaptive-Filtering Algorithms Using Gradient-Based Step Size," Circuits and Systems I: Regular Papers, IEEE Transactions on , vol.61, no.7, pp.2048,2056, July 2014. A new class of affine-projection-like (APL) adaptive-filtering algorithms is proposed. The new algorithms are obtained by eliminating the constraint of forcing the a posteriori error vector to zero in the affine-projection algorithm proposed by Ozeki and Umeda. In this way, direct or indirect inversion of the input signal matrix is not required and, consequently, the amount of computation required per iteration can be reduced. In addition, as demonstrated by extensive simulation results, the proposed algorithms offer reduced steady-state misalignment in system-identification, channel-equalization, and acoustic-echo-cancelation applications. A mean-square-error analysis of the proposed APL algorithms is also carried out and its accuracy is verified by using simulation results in a system-identification application.
    Keywords: adaptive filters; gradient methods; mean square error methods; a posteriori error vector; acoustic-echo-cancelation applications; affine-projection-like adaptive-filtering algorithms; channel-equalization; gradient-based step size; input signal matrix; mean-square-error analysis; steady-state misalignment; system-identification; Algorithm design and analysis; Computational complexity; Convergence; Least squares approximations; Matrix decomposition; Steady-state; Vectors; Adaptive filters; adaptive-filtering algorithms; affine-projection algorithms; mean-square error in adaptive filtering (ID#:14-2036)
  • Bin Sun; Shutao Li; Jun Sun, "Scanned Image Descreening With Image Redundancy and Adaptive Filtering," Image Processing, IEEE Transactions on , vol.23, no.8, pp.3698,3710, Aug. 2014. Currently, most electrophotographic printers use halftoning technique to print continuous tone images, so scanned images obtained from such hard copies are usually corrupted by screen like artifacts. In this paper, a new model of scanned halftone image is proposed to consider both printing distortions and halftone patterns. Based on this model, an adaptive filtering based descreening method is proposed to recover high quality contone images from the scanned images. Image redundancy based denoising algorithm is first adopted to reduce printing noise and attenuate distortions. Then, screen frequency of the scanned image and local gradient features are used for adaptive filtering. Basic contone estimate is obtained by filtering the denoised scanned image with an anisotropic Gaussian kernel, whose parameters are automatically adjusted with the screen frequency and local gradient information. Finally, an edge-preserving filter is used to further enhance the sharpness of edges to recover a high quality contone image. Experiments on real scanned images demonstrate that the proposed method can recover high quality contone images from the scanned images. Compared with the state-of-the-art methods, the proposed method produces very sharp edges and much cleaner smooth regions.
    Keywords: Gaussian processes; adaptive filters; electrophotography; image denoising; printers; adaptive filtering; anisotropic Gaussian kernel; continuous tone image print; denoising algorithm; edge-preserving filter; electrophotographic printers; halftone pattern; high quality contone image recovery ;image redundancy ;local gradient features; printing distortion; printing noise reduction; scanned halftone image; scanned image descreening; Image edge detection; Kernel; Noise; Noise reduction; Printers; Printing; Redundancy; Scanned image; adaptive filtering; descreening; inverse halftoning; steerable filter (ID#:14-2037)
  • Arablouei, R.; Werner, S.; Dogancay, K., "Analysis of the Gradient-Descent Total Least-Squares Adaptive Filtering Algorithm," Signal Processing, IEEE Transactions on , vol.62, no.5, pp.1256,1264, March1, 2014. The gradient-descent total least-squares (GD-TLS) algorithm is a stochastic-gradient adaptive filtering algorithm that compensates for error in both input and output data. We study the local convergence of the GD-TLS algorithm and find bounds for its step-size that ensure its stability. We also analyze the steady-state performance of the GD-TLS algorithm and calculate its steady-state mean-square deviation. Our steady-state analysis is inspired by the energy-conservation-based approach to the performance analysis of adaptive filters. The results predicted by the analysis show good agreement with the simulation experiments.
    Keywords: adaptive filters; least squares approximations; stochastic processes; energy-conservation; gradient-descent total least-squares algorithm; steady-state analysis; steady-state mean-square deviation; stochastic-gradient adaptive filtering algorithm; Adaptive filters; Algorithm design and analysis; Signal processing algorithms; Stability criteria; Steady-state; Vectors; Adaptive filtering; Rayleigh quotient; mean-square deviation; performance analysis; stability; total least-squares (ID#:14-2038)
  • Thu Trang Le; Atto, AM.; Trouve, E.; Nicolas, J.-M., "Adaptive Multitemporal SAR Image Filtering Based on the Change Detection Matrix," Geoscience and Remote Sensing Letters, IEEE , vol.11, no.10, pp.1826,1830, Oct. 2014. This letter presents an adaptive filtering approach of synthetic aperture radar (SAR) image times series based on the analysis of the temporal evolution. First, change detection matrices (CDMs) containing information on changed and unchanged pixels are constructed for each spatial position over the time series by implementing coefficient of variation (CV) cross tests. Afterward, the CDM provides for each pixel in each image an adaptive spatiotemporal neighborhood, which is used to derive the filtered value. The proposed approach is illustrated on a time series of 25 ascending TerraSAR-X images acquired from November 6, 2009 to September 25, 2011 over the Chamonix-Mont-Blanc test-site, which includes different kinds of change, such as parking occupation, glacier surface evolution, etc.
    Keywords: adaptive filters; matrix algebra; radar detection; radar imaging;s ynthetic aperture radar; time series; CDM; CV; Chamonix-Mont-Blanc test-site; TerraSAR-X image acquisition; adaptive multitemporal SAR image filtering approach; adaptive spatiotemporal neighborhood; change detection matrix; coefficient of variation; glacier surface evolution; parking occupation; synthetic aperture radar ;temporal evolution analysis ;time series; Filtering;Indexes; Noise; Remote sensing; Speckle; Synthetic aperture radar; Time series analysis; Change detection; coefficient of variation (CV);synthetic aperture radar (SAR) image time series; temporal adaptive filtering (ID#:14-2039)
  • Zerguine, A; Hammi, O.; Abdelhafiz, AH.; Helaoui, M.; Ghannouchi, F., "Behavioral modeling and predistortion of nonlinear power amplifiers based on adaptive filtering techniques," Multi-Conference on Systems, Signals & Devices (SSD), 2014 11th International , vol., no., pp.1,5, 11-14 Feb. 2014. In this paper, the use of some of the most popular adaptive filtering algorithms for the purpose of linearizing power amplifiers by the well-known digital predistortion (DPD) technique is investigated. First, an introduction to the problem of power amplifier linearization is given, followed by a discussion of the model used for this purpose. Next, a variety of adaptive algorithms are used to construct the digital predistorter function for a highly nonlinear power amplifier and their performance is comparatively analyzed. Based on the simulations presented in this paper, conclusions regarding the choice of algorithm are derived.
    Keywords: adaptive filters; power amplifiers; DPD technique; adaptive filtering techniques; behavioral modeling; digital predistortion technique; nonlinear power amplifier predistortion; power amplifier linearization; Adaptation models; Wireless communication; Adaptive filtering; behavioral modeling; nonlinear system identification; power amplifier nonlinearities (ID#:14-2040)
  • Wei Zhu; Jun Tang; Shuang Wan; Jie-Li Zhu, "Outlier-resistant adaptive filtering based on sparse Bayesian learning," Electronics Letters , vol.50, no.9, pp.663,665, April 24 2014. In adaptive processing applications, the design of the adaptive filter requires estimation of the unknown interference-plus-noise covariance matrix from secondary training data. The presence of outliers in the training data can severely degrade the performance of adaptive processing. By exploiting the sparse prior of the outliers, a Bayesian framework to develop a computationally efficient outlier-resistant adaptive filter based on sparse Bayesian learning (SBL) is proposed. The expectation-maximisation (EM) algorithm is used therein to obtain a maximum a posteriori (MAP) estimate of the interference-plus-noise covariance matrix. Numerical simulations demonstrate the superiority of the proposed method over existing methods.
    Keywords: Bayes methods; adaptive filters; covariance matrices; expectation-maximization algorithm; filtering theory; interference (signal);learning (artificial intelligence);EM algorithm; MAP estimation; SBL; adaptive processing applications; expectation-maximisation algorithm; maximum a posteriori estimation; outlier-resistant adaptive filtering; secondary training data; sparse Bayesian learning; unknown interference-plus-noise covariance matrix estimation (ID#:14-2041)
  • Shi, L.; Lin, Y., "Convex Combination of Adaptive Filters under the Maximum Correntropy Criterion in Impulsive Interference," Signal Processing Letters, IEEE , vol.21, no.11, pp.1385,1388, Nov. 2014. A robust adaptive filtering algorithm based on the convex combination of two adaptive filters under the maximum correntropy criterion (MCC) is proposed. Compared with conventional minimum mean square error (MSE) criterion-based adaptive filtering algorithm, the MCC-based algorithm shows a better robustness against impulsive interference. However, its major drawback is the conflicting requirements between convergence speed and steady-state mean square error. In this letter, we use the convex combination method to overcome the tradeoff problem. Instead of minimizing the squared error to update the mixing parameter in conventional convex combination scheme, the method of maximizing the correntropy is introduced to make the proposed algorithm more robust against impulsive interference. Additionally, we report a novel weight transfer method to further improve the tracking performance. The good performance in terms of convergence rate and steady-state mean square error is demonstrated in plant identification scenarios that include impulsive interference and abrupt changes.
    Keywords: adaptive filtering; convex combination ;impulsive interference; maximum correntropy criterion; weight transfer (ID#:14-2042)
  • Tong Liu; Xu, Qian; Li, Yuejun, "Adaptive filtering design for in-motion alignment of INS," Control and Decision Conference (2014 CCDC), The 26th Chinese on , vol., no., pp.2669,2674, May 31 2014-June 2 2014. Misalignment angles estimation of strapdown inertial navigation system (INS) using global positioning system (GPS) data is highly affected by measurement noises, especially with noises displaying time varying statistical properties. Hence, adaptive filtering approach is recommended for the purpose of improving the accuracy of in-motion alignment. In this paper, a simplified form of Celso's adaptive stochastic filtering is derived and applied to estimate both the INS error states and measurement noise statistics. To detect and bound the influence of outliers in INS/GPS integration, outlier detection based on jerk tracking model is also proposed. The accuracy and validity of the proposed algorithm is tested through ground based navigation experiments.
    Keywords: Global Positioning System Kalman filters; Mathematical model; Noise; Noise measurement; Vehicles; INS/GPS integration; adaptive filtering ;in-motion alignment; outlier detection (ID#:14-2043)
  • Tuia, D.; Munoz-Mari, J.; Rojo-Alvarez, J.L.; Martinez-Ramon, M.; Camps-Valls, G., "Explicit Recursive and Adaptive Filtering in Reproducing Kernel Hilbert Spaces," Neural Networks and Learning Systems, IEEE Transactions on , vol.25, no.7, pp.1413,1419, July 2014. This brief presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces. Unlike previous approaches that exploit the kernel trick on filtered and then mapped samples, we explicitly define the model recursivity in the Hilbert space. For that, we exploit some properties of functional analysis and recursive computation of dot products without the need of preimaging or a training dataset. We illustrate the feasibility of the methodology in the particular case of the g-filter, which is an infinite impulse response filter with controlled stability and memory depth. Different algorithmic formulations emerge from the signal model. Experiments in chaotic and electroencephalographic time series prediction, complex nonlinear system identification, and adaptive antenna array processing demonstrate the potential of the approach for scenarios where recursivity and nonlinearity have to be readily combined.
    Keywords: Hilbert spaces; IIR filters; adaptive filters; recursive filters; stability; time series; adaptive antenna array processing; adaptive filtering; chaotic time series prediction; complex nonlinear system identification; controlled stability; electroencephalographic time series prediction; functional analysis; infinite impulse response filter; kernel Hilbert spaces; memory depth; recursive filtering; adaptation models; Hilbert space; Kernel; Mathematical model; Time series analysis; Training; Vectors; Adaptive; autoregressive and moving-average; filter; kernel methods; recursive; (ID#:14-2044)
  • Shimauchi, Suehiro; Ohmuro, Hitoshi, "Accurate adaptive filtering in square-root Hann windowed short-time fourier transform domain," Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on , vol., no., pp.1305,1309, 4-9 May 2014. A novel short-time Fourier transform (STFT) domain adaptive filtering scheme is proposed that can be easily combined with nonlinear post filters such as residual echo or noise reduction in acoustic echo cancellation. Unlike normal STFT subband adaptive filters, which suffers from aliasing artifacts due to its poor prototype filter, our scheme achieves good accuracy by exploiting the relationship between the linear convolution and the poor prototype filter, i.e., the STFT window function. The effectiveness of our scheme was confirmed through the results of simulations conducted to compare it with conventional methods.
    Keywords: Adaptive filters ;acoustic echo cancellation; short-time Fourier transform; square-root Hann window (ID#:14-2045)


Articles listed on these pages have been found on publicly available internet pages and are cited with links to those pages. Some of the information included herein has been reprinted with permission from the authors or data repositories. Direct any requests via Email to SoS.Project (at) for removal of the links or modifications to specific citations. Please include the ID# of the specific citation in your correspondence.