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Programmable canonical correlation analysis: A flexible framework for blind adaptive spatial filtering

IEEE TRANSACTIONS ON SIGNAL PROCESSING 1995年第12期43卷 2898-2908页

作者： Schell, SV Gardner, WA UNIV CALIF DAVIS DEPT ELECT & COMP ENGN DAVIS CA 95616 USA

We present a new framework known as the programmable canonical correlation analysis (PCCA) for the design of blind adaptive spatial filtering algorithms that attempt to separate one or more signals of interest from unknown cochannel interference and noise. Unlike many alternatives, PCCA does not require knowledge of the calibration data for the array, directions of arrival, training signals, or spatial autocorrelation matrices of the the noise or interferers. A novel aspect of PCCA is the ease with which new algorithms, targeted at capturing all signals from particular classes of interest, can be developed within this framework. In this paper, several existing algorithms are unified within the PCCA framework, and new algorithms are derived as examples. Analysis for the infinite-collect case and simulation for the finite-collect case illustrate the operation of specific algorithms within the PCCA framework.

关键词： Adaptive filters Algorithm design and analysis Sensor arrays Signal analysis Signal design filtering algorithms Calibration Adaptive arrays Sensor systems and applications Intelligent sensors

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Innovative Interaction Approach in IMM filtering for Vehicle Motion Models With Unequal States Dimension

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第4期71卷 3579-3594页

作者： Zubaca, Jasmina Stolz, Michael Seeber, Richard Schratter, Markus Watzenig, Daniel Graz Univ Technol Inst Automat & Control A-8010 Graz Austria Virtual Vehicle Res GmbH A-8010 Graz Austria

Robust and adaptive vehicle state estimation and tracking algorithms have become a very important part within the autonomous driving field. The family of interacting multiple model (IMM) filters has shown to provide very effective and accurate state estimation in systems whose behavior patterns change significantly over time. The main reason for the improved performance of IMM filters compared to single model approaches is the mode mixing, which constantly aligns the different models. This paper proposes an innovative way for the mode mixing, when the state-vectors of the models are of different size. The proposed mixing approach consists of two parts: firstly mixing the common states and secondly weighting between original and mixed states based on the model probabilities. Results from artificial simulations and real world measurement setups are shown to demonstrate the validity of the approach. Compared to previously suggested solutions, the proposed approach is more general and the overall complexity of the mode mixing step is reduced, which is the main contribution of the presented paper.

关键词： State estimation filtering algorithms Tracking Data models Autonomous vehicles Noise measurement Motion estimation Autonomous driving interacting multiple model filter mode mixing state estimation vehicle motion models vehicle tracking

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Unbiased Finite Impluse Response filtering AN ITERATIVE ALTERNATIVE TO KALMAN filtering IGNORING NOISE AND INITIAL CONDITIONS

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IEEE CONTROL SYSTEMS MAGAZINE 2017年第5期37卷 70-89页

作者： Shmaliy, Yuriy S. Zhao, Shunyi Ahn, Choon Ki Univ Guanajuato Dept Elect Engn Guanajuato Mexico Univ Alberta Dept Chem & Mat Engn Edmonton AB Canada Jiangnan Univ Wuxi Peoples R China Korea Univ Coll Engn Excellence Seoul South Korea Korea Univ Sch Elect Engn Seoul South Korea

If a system and its observation are both represented in state space with linear equations, the system noise and the measurement noise are white, Gaussian, and mutually uncorrelated, and the system and measurement noise statistics are known exactly; then, a Kalman filter (KF) [1] with the same order as the system provides optimal state estimates in a way that is simple and fast and uses little memory. Because such estimators are of interest for designers, numerous linear and nonlinear problems have been solved using the KF, and many articles about KF applications appear every year. However, the KF is an infinite impulse response (IIR) filter [2]. Therefore, the KF performance may be poor if operational conditions are far from ideal [3]. Researchers working in the field of statistical signal processing and control are aware of the numerous issues facing the use of the KF in practice: insufficient robustness against mismodeling [4] and temporary uncertainties [2], the strong effect of the initial values [1], and high vulnerability to errors in the noise statistics [5]-[7].

关键词： Finite impulse response filters Transversal filters filtering algorithms IIR filters Robustness Noise measurement Tutorials

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Bi-iteration recursive instrumental variable subspace tracking and adaptive filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1998年第10期46卷 2708-2725页

作者： Strobach, P Fachhsch Furtwangen Rohrnbach Germany

In this paper, we propose a class of fast sequential bi-iteration singular value (Bi-SVD) subspace tracking algorithms for adaptive eigendecomposition of the cross covariance matrix in the recursive instrumental variable (RIV) method of system identification. These. algorithms can be used for RIV subspace processing of signals in unknown correlated Gaussian noise, Realizations with O(Nr(2)) and O(Nr) operations per time step are described, where N is the input vector dimension, and r is the number of dominant singular values and vectors to be tracked. The algorithms are solely based on passive Givens plane rotations and standard matrix-vector multiplications. The matrix inversion lemma is not used. The application and performance of the algorithms is demonstrated in a low-rank RIV subspace adaptive filtering context.

关键词： Instruments Adaptive filters Covariance matrix Frequency estimation Direction of arrival estimation filtering algorithms Gaussian noise Working environment noise System identification Multiple signal classification

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ADAPTIVE COMB filtering FOR HARMONIC SIGNAL ENHANCEMENT

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1986年第5期34卷 1124-1138页

作者： NEHORAI, A PORAT, B TECHNION ISRAEL INST TECHNOL DEPT ELECT ENGNIL-32000 HAIFAISRAEL SYST CONTROL TECHNOL INC PALO ALTOCA 94304

A new algorithm is presented for adaptive comb filtering and parametric spectral estimation of harmonic signals with additive white noise. The algorithm is composed of two cascaded parts. The first estimates the fundamental frequency and enhances the harmonic component in the input, and the second estimates the harmonic amplitudes and phases. Performance analysis provides new results for the asymptotic Cramer-Rao bound (CRB) on the parameters of harmonic signals with additive white noise. Results of simulations indicate that the variances of the estimates are of the same order of magnitude as the CRB for sufficiently large data sets, and illustrate the performance in enhancing noisy artificial periodic signals.

关键词： Adaptive filters Power harmonic filters Frequency estimation Finite impulse response filter Amplitude estimation Phase estimation Band pass filters Additive white noise filtering algorithms Performance analysis

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Non-fragile H_∞ fuzzy filtering for discrete-time non-linear systems

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IET CONTROL THEORY AND APPLICATIONS 2013年第6期7卷 848-857页

作者： Ding, Da-Wei Li, Xiaoli Wang, Youyi Shi, Zhiguo Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

This study is concerned with the problem of non-fragile H-infinity filtering for discrete-time non-linear systems in Takagi-Sugeno's form. Additive interval uncertainty that reflects imprecision in filter implementation because of finite word length effects is considered. Based on vertex theory and probabilistic robust approach, deterministic and randomised filtering algorithms are proposed, respectively. Compared with the deterministic algorithm, the randomised algorithm has acceptable computational complexity, especially for high-dimensional systems. Two examples are given to illustrate the effectiveness of the proposed algorithms.

关键词： deterministic algorithms fuzzy filter randomized algorithms filtering algorithms complexity classes discrete-time Filtration word length

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ON THE DUALITY BETWEEN FAST QR METHODS AND LATTICE METHODS IN LEAST-SQUARES ADAPTIVE filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1991年第4期39卷 879-891页

作者： REGALIA, PA BELLANGER, MG TELECOMMUN RADIOELECTR & TELEPHON F-92350 LE PLESSIS ROBINSFRANCE

Recent attention in least squares adaptive filtering has focused on methods derived from the QR factorization of matrices, owing to the development of a family of "fast" algorithms to execute its recursive computation, combined with the apparent stability of the algorithms. A useful property of the QR method is that, upon solving an Nth order filtering problem, the solutions to all lower order problems are obtained as a by-product. This property is also found in lattice least squares filters, and the present paper shows that the two are indeed duals, and follow from identical geometric principles. Whereas the lattice methods compute the residuals of a projection operation via the forward and backward prediction errors, the QR methods compute instead the weights used in the projections. Within this framework, we solve for the first time the parameter identification problem using fast QR methods by showing that the reflection coefficients and tap parameters of a least squares lattice filter operating in the joint process mode are immediately available as internal variables in the fast QR algorithms. This parameter set can be readily exploited in system identification, signal analysis, and linear predictive coding, for example. The relations derived in this paper also lead to a new fast least squares algorithm of minimal complexity, which is a hybrid between a QR and a lattice algorithm. The algorithm combines the order recursive properties of the lattice approach with the robust numerical behavior of the QR approach. It is hoped that the connections exposed in this paper will expand the range of applications of the fast QR algorithms.

关键词： Lattices Least squares methods Adaptive filters Signal processing algorithms Transversal filters filtering algorithms Robustness Adaptive algorithm Least squares approximation Stability

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USDnet: Unsupervised Speech Dereverberation via Neural Forward filtering

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2024年 32卷 3882-3895页

作者： Wang, Zhong-Qiu Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

In reverberant conditions with a single speaker, each far-field microphone records a reverberant version of the same speaker signal at a different location. In over-determined conditions, where there are multiple microphones but only one speaker, each recorded mixture signal can be leveraged as a constraint to narrow down the solutions to target anechoic speech and thereby reduce reverberation. Equipped with this insight, we propose USDnet, a novel deep neural network (DNN) approach for unsupervised speech dereverberation (USD). At each training step, we first feed an input mixture to USDnet to produce an estimate for target speech, and then linearly filter the DNN estimate to approximate the multi-microphone mixture so that the constraint can be satisfied at each microphone, thereby regularizing the DNN estimate to approximate target anechoic speech. The linear filter can be estimated based on the mixture and DNN estimate via neural forward filtering algorithms such as forward convolutive prediction. We show that this novel methodology can promote unsupervised dereverberation of single-source reverberant speech.

关键词： Training Microphones Mathematical models Reverberation filtering algorithms Nonlinear filters Noise measurement Unsupervised neural speech dereverberation (USD)

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GRADIENT-BASED ADAPTIVE IIR NOTCH filtering FOR FREQUENCY ESTIMATION

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1990年第5期38卷 769-777页

作者： CHICHARO, JF NG, TS Department of Electrical and Computer Engineering University of Wollongong Wollongong NSW Australia

The use of gradient-based algorithms with infinite impulse response (IIR) notch filtering for estimating sinusoids imbedded in noise is investigated. Two notch filter model structures are presented. The first is for applications where two signal sources with correlated noise components can be assessed. The second can be used in situations where only one composite signal source is available. Error surface analysis indicates that second-order modules are unimodal and result in guaranteed convergence. Higher-order modules are multimodal and require judicious choice of initial parameter estimates. Simulation results are included to demonstrate the performance characteristics of both filter structures.< >

关键词： Adaptive filters IIR filters Frequency estimation Signal processing algorithms Convergence Digital filters Australia Error analysis filtering algorithms Interference

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Research on Anomaly Suppression Correlation filtering Algorithm

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IEEE ACCESS 2022年 10卷 2457-2468页

作者： Ren, Hongge Cui, Yin Shi, Tao Tianjin Chengjian Univ Sch Control & Mech Engn Tianjin 300384 Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan 063210 Peoples R China Tianjin Univ Technol Sch Elect Engn & Automat Tianjin 300384 Peoples R China

Aiming at the problems of target occlusion, illumination change, and fast motion in target tracking, an anomaly suppression correlation filtering algorithm is proposed. The histogram of oriented gradient (HOG) feature and color name (CN) feature are extracted to construct the target appearance model, and an abnormal suppression correlation filter is constructed to suppress sudden changes in the filter response graph. The alternating direction method of multipliers (ADMM) method is used to speed up the calculation of the filter. A scale filter is added in the article to solve the problem of inaccurate tracking caused by a single scale. In the model update process, the three-layer rotating circle memory model is used to update, which improves the tracking ability of the algorithm. It is better to update the target model in the scene where the target is occluded. The proposed algorithm and BACF, KCC, KCF, MKCFup, ARCF are tested in the OTB50, OTB100, UAV123, TC128 experimental data sets. The results show that the visual tracking algorithm proposed in the article has a high success rate and accuracy, and has certain research value.

关键词： Target tracking filtering algorithms Correlation Feature extraction Training Information filters Real-time systems Computer vision target tracking correlation filtering abnormal suppression ADMM method memory model

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