Class Of Adaptive Filters Research Articles

On the Regularization of Recursive Least-Squares Adaptive Algorithms Using Line Search Methods

Stereophonic acoustic echo cancellation (SAEC) requires the identification of four unknown impulse responses corresponding to four loudspeaker-to-microphone pairs. Recent developments in the field of adaptive filters for SAEC setups have allowed for the handling of a single complex-valued adaptive impulse response, instead of the four classical real-valued adaptive filters. With the simplified framework provided by the widely linear (WL) model, more advanced versions of recursive least-squares (RLS) were employed in order to take advantage of their superior tracking speeds when working with highly correlated input signals (such as speech). Despite the performances and numerical stability provided by using exponentially weighted versions of the RLS family in combination with line search methods (LSMs), the SAEC configurations have limited capabilities in mitigating the negative effects caused by high-level disturbances affecting the two microphone signals. Such is the case of double-talk scenarios, which considerably reduce the update accuracy of the adaptive system. This paper analyzes a regularization technique for the named WL-RLS-LSM adaptive filters by adjusting the correlation matrix associated with the input signals and creating a reaction in the update process. The proposed method is designed to considerably slow (or even freeze) the adaptation process while the disturbance is manifested. Simulation results are discussed in order to validate the theoretical content.

Variational Bayesian Based IMM Robust GPS Navigation Filter

This paper investigates the navigational performance of Global Positioning System (GPS) using the variational Bayesian (VB) based robust filter with interacting multiple model (IMM) adaptation as the navigation processor. The performance of the state estimation for GPS navigation processing using the family of Kalman filter (KF) may be degraded due to the fact that in practical situations the statistics of measurement noise might change. In the proposed algorithm, the adaptivity is achieved by estimating the time-varying noise covariance matrices based on VB learning using the probabilistic approach, where in each update step, both the system state and time-varying measurement noise were recognized as random variables to be estimated. The estimation is iterated recursively at each time to approximate the real joint posterior distribution of state using the VB learning. One of the two major classical adaptive Kalman filter (AKF) approaches that have been proposed for tuning the noise covariance matrices is the multiple model adaptive estimate (MMAE). The IMM algorithm uses two or more filters to process in parallel, where each filter corresponds to a different dynamic or measurement model. The robust Huber's M-estimation-based extended Kalman filter (HEKF) algorithm integrates both merits of the Huber M-estimation methodology and EKF. The robustness is enhanced by modifying the filter update based on Huber's M-estimation method in the filtering framework. The proposed algorithm, referred to as the interactive multi-model based variational Bayesian HEKF (IMM-VBHEKF), provides an effective way for effectively handling the errors with time-varying and outlying property of non-Gaussian interference errors, such as the multipath effect. Illustrative examples are given to demonstrate the navigation performance enhancement in terms of adaptivity and robustness at the expense of acceptable additional execution time.

Diffusion Generalized MCC with a Variable Center Algorithm for Robust Distributed Estimation

Classical adaptive filtering algorithms with a diffusion strategy under the mean square error (MSE) criterion can face difficulties in distributed estimation (DE) over networks in a complex noise environment, such as non-zero mean non-Gaussian noise, with the object of ensuring a robust performance. In order to overcome such limitations, this paper proposes a novel robust diffusion adaptive filtering algorithm, which is developed by using a variable center generalized maximum Correntropy criterion (GMCC-VC). Generalized Correntropy with a variable center is first defined by introducing a non-zero center to the original generalized Correntropy, which can be used as robust cost function, called GMCC-VC, for adaptive filtering algorithms. In order to improve the robustness of the traditional MSE-based DE algorithms, the GMCC-VC is used in a diffusion adaptive filter to design a novel robust DE method with the adapt-then-combine strategy. This can achieve outstanding steady-state performance under non-Gaussian noise environments because the GMCC-VC can match the distribution of the noise with that of non-zero mean non-Gaussian noise. The simulation results for distributed estimation under non-zero mean non-Gaussian noise cases demonstrate that the proposed diffusion GMCC-VC approach produces a more robustness and stable performance than some other comparable DE methods.

BEM Adaptive filtering for SI cancellation in full-duplex underwater acoustic systems

Self-interference (SI) cancellation (SIC) is the key technology for achieving full-duplex (FD) communications in underwater acoustic systems. In practice, SI channels are often fast-varying, e.g., due to reflections from surface waves. Classical adaptive filters, such as the recursive least squares (RLS) algorithm, predict the channel impulse response when used for channel estimation. If a tracking delay is acceptable, interpolating estimators capable of providing more accurate estimates of time-varying impulse responses can be used. Interpolating estimators with good tracking performance are normally of high complexity. In this paper, we propose low-complexity interpolating adaptive filters which combine the basis expansion model (BEM) approach with the sliding-window RLS (SRLS) algorithm. Specifically, we use the Legendre polynomials as the basis functions and solve the system of equations using dichotomous coordinate descent (DCD) iterations, thus the name the SRLS-L-DCD adaptive filter. A sparse algorithm (HSRLS-L-DCD) based on homotopy iterations is then proposed to exploit the sparsity in the expansion coefficients. The identification performance of the adaptive filters is investigated by a simulation which mimics an FD lake experiment. Both the simulation and lake experiments show that significant improvement in the SIC performance is achieved with the proposed low-complexity algorithms compared to the classical SRLS algorithm.

An Adaptive Multi-Dimensional Vehicle Driving State Observer Based on Modified Sage-Husa UKF Algorithm.

An accurate vehicle driving state observer is a necessary condition for a safe automotive electronic control system. Vehicle driving state observer is challenged by unknown measurement noise and transient disturbances caused by complex working conditions and sensor failure. For the classical adaptive unscented Kalman filter (AUKF) algorithm, transient disturbances will cause the failure of state estimation and affect the subsequent process. This paper proposes an AUKF based on a modified Sage–Husa filter and divergence calculation technique for multi-dimensional vehicle driving state observation. Based on the seven-degrees-of-freedom vehicle model and the Dugoff tire model, the proposed algorithm corrects the measurement noise by using modified Sage–Husa maximum posteriori. To reduce the influence of transient disturbance on the subsequent process, covariance matrix is updated after divergence is detected. The effectiveness of the algorithm is tested on the double lane change and Sine Wave road conditions. The robustness of the algorithm is tested under severe transient disturbance. The results demonstrate that the modified Sage–Husa UKF algorithm can accurately detect transient disturbance and effectively reduce the resulted accumulated error. Compared to classical AUKF, our algorithm significantly improves the accuracy and robustness of vehicle driving state estimation. The research in this paper provides a reference for multi-dimensional data processing under changeable vehicle driving states.

Nonlinear acoustic echo cancellation with kernelized adaptive filters

A well-known problem in speech communication is the occurrence of acoustic echo in hands-free telephony. There are several classical adaptive filters that have been used as a stand alone approach for linear acoustic echo cancellation(AEC). A common assumption in most AEC techniques is that the echo path is linear. However, in real scenarios, owing to distortions introduced by other acoustic artefacts in handheld devices, the echo path is nonlinear. Therefore, there is a need to employ a nonlinear echo canceller. In this paper, a kernel expansion on a family of adaptive filtering algorithms based on least mean square filter is proposed. Kernel methods help model the nonlinear echo path and attain global minimum easily by finding the optimal set of filter weights. The simulations are carried out for speech signal with and without noise under different signal-to-noise ratio values. The results show that the proposed method achieves a significant improvement in a nonlinear acoustic echo cancellation environment in terms of echo return loss enhancement.

Adaptive Filtering for Full-Duplex UWA Systems With Time-Varying Self-Interference Channel

To enable full-duplex (FD) in underwater acoustic (UWA) systems, a high level of self-interference cancellation (SIC) is required. This can be achieved by using a combination of SIC methods, including digital SIC. For digital SIC, adaptive filters are used. In time-invariant channels, the SI can be effectively cancelled by classical recursive least-square (RLS) adaptive filters, such as the sliding-window RLS (SRLS) or exponential-window RLS, but their SIC performance degrades in time-varying channels, e.g., in channels with a moving sea surface. Their performance can be improved by delaying the filter inputs. This delay, however, makes the mean squared error (MSE) unsuitable for measuring the SIC performance. In this article, we propose a new evaluation metric, the SIC factor (SICF), which gives better indication of the SIC performance compared to MSE. The SICF can be used to evaluate the performance of digital SIC techniques without the need of implementing a full FD system. A new SRLS adaptive filter based on parabolic approximation of the channel variation in time, named SRLS-P, is also proposed. The SIC performance of the SRLS-P adaptive filter and classical RLS algorithms (with and without the delay) is evaluated by simulation and in lake experiments. The results show that the SRLS-P adaptive filter can significantly improve the SIC performance, compared to the classical RLS adaptive filters.

Dual Deep Neural Network Based Adaptive Filter for Estimating Absolute Longitudinal Speed of Vehicles

This study employs a dual deep neural network (D-DNN) to accurately estimate the absolute longitudinal speed of a vehicle. Accuracy in speed estimation is crucial for vehicle safety, because longitudinal speed is a common parameter employed as a state variable in active safety systems such as anti-lock braking system and traction control system. In this study, DNNs are applied to determine the gain of an adaptive filter to estimate vehicle speed. The used data consists of longitudinal acceleration, wheel speed, filter gain, and estimated vehicle speed. The data generated from Carsim software are collected and preprocessed using a Simulink model. To acquire data with numerous wheel slip patterns, various acceleration and deceleration conditions are applied to four different road conditions. Though, it is challenging to achieve a single DNN model that is optimally cope with the various driving situations. Thus, we adopt two DNN models that were individually trained in low and high acceleration regions. The dual DNN model results in error reductions of 74% and 65%, compared with a single DNN and classical adaptive Kalman filter approaches, respectively.

Range-Doppler Fast Block LMS algorithm for a DVB-T-based Passive Bistatic Radar

In this paper, we propose a novel algorithm based on fast block LMS (least mean square) adaptive filter, called RD-FBLMS (range-Doppler fast block LMS) for direct path and multipath interferences cancellation in DVB-T (digital video broadcasting—terrestrial) passive bistatic radars (PBRs). These interferences represent some of the major problems in PBR systems, generated by signals received from the illuminator of opportunity and their multiple reflections through the surveillance channel, which is only dedicated to receive targets echoes. It is known that classical adaptive filtering can only be used for cancellation of zero Doppler interferences. Based on successive reference signal modulations and suppressions of nonzero Doppler multipath interferences, the RD-FBLMS algorithm exhibits a faster convergence rate, a shorter processing time and a better cross-ambiguity function than well-known existing algorithms.

A novel 2D-ABC adaptive filter algorithm: A comparative study

Recently, two dimensional (2D) adaptive filter, which can self-adjust the filter coefficients by using an optimization algorithm driven by an error function, has attracted much attention by researchers and practitioners, because 2D adaptive filtering can be employed in many image processing applications, such as image denoising, enhancement and deconvolution. In this paper, a novel 2D artificial bee colony (2D-ABC) adaptive filter algorithm was firstly proposed and to the best of our knowledge, there is no study describing 2D adaptive filter algorithm based on metaheuristic algorithms in the literature. At the first stage, in order to analyze the performance and computational efficiency of the novel 2D-ABC adaptive filter algorithm, it was used in the 2D adaptive noise cancellation (ANC) as recommend in literature. For a fair comparison, the competitor 2D adaptive filter algorithms were applied to the same 2D-ANC setup under same condition, such as same Gaussian noise, same filter order or same test images. The results of the novel 2D-ABC adaptive filter algorithm were compared with those of the 2D affine projection algorithms (APA), 2D normalized least mean square (NLMS) and 2D least mean square (LMS) adaptive filter algorithms. At the second stage, to demonstrate the robustness of the novel 2D-ABC adaptive filter algorithm, it was implemented for speckle noise filtering on noisy clinical ultrasound images. The results show that the novel 2D-ABC adaptive filter algorithm has a better performance than the other classical adaptive filter algorithms and its denoising efficiency is quite well on noisy images with different characteristics.

基于分层滤波技术的冲击噪声检测与去除算法

本文提出了一种基于分层滤波技术的图像随机值冲击噪声检测和去除算法。为了解决经典自适应中心加权中值滤波技术在较高噪声比率下漏检严重的问题，我们采取逐级设置由高到低的噪声值判决门限来层层筛选噪声，利用不同高度的门限甄别出不同信赖度的冲击噪点，同时更新已检测出的噪声点值。每一层均得到一幅中间去噪图像，并继续进行噪声点的判决，最终得到完整的噪声点空间以及终极去噪图像。通过对多幅不同噪声比率的噪声图进行图像恢复的仿真实验验证后，得出结论：本文所提算法无论在低噪声比率还是高噪声比率的情况下均能有效地检测出图像中的随机值冲击噪声，漏检和误检像素数相对较低，同时获得了较好的图像去噪效果，极大地扩展了原算法的应用范围。 In this paper, an algorithm about random-valued impulse noise detection and removal based on the hierarchical filter technology is proposed. In order to solve the problem of severe miss detection of classical adaptive center-weighted median filter in the situation of high noise ratios, we set the noise judgment thresholds from high values to low values to select noisy pixels hierarchically. The impulse noise with different reliable degrees is selected through different thresholds and at the same time the detected pixel values are updated. The interim denoised image is generated in each denoising layer and the noise detection continues in the next denoising layer. Lastly, we get all the locations of noisy pixels and the final denoised image is obtained. Extensive experimental results for different noisy images with different noise ratios show that our proposed algorithm can obtain the good performance of random-valued impulse noise detection in the situations of not only low noise ratios but also high noise ratios. The miss and false noise detection ratios are both low relatively. At the same time, the good denoised images can be obtained. Our algorithm expands the application range of the classical adaptive center-weighted median filter.

A New Subband Adaptive Filtering Algorithm for Sparse System Identification with Impulsive Noise

This paper presents a novel subband adaptive filter (SAF) for system identification where an impulse response is sparse and disturbed with an impulsive noise. Benefiting from the uses ofl1-norm optimization andl0-norm penalty of the weight vector in the cost function, the proposedl0-norm sign SAF (l0-SSAF) achieves both robustness against impulsive noise and remarkably improved convergence behavior more than the classical adaptive filters. Simulation results in the system identification scenario confirm that the proposedl0-norm SSAF is not only more robust but also faster and more accurate than its counterparts in the sparse system identification in the presence of impulsive noise.

The novel two-dimensional adaptive filter algorithms with the performance analysis

Two-dimensional (2D) adaptive filtering is a technique that can be applied to many images, and signal processing applications. This paper extends the one-dimensional adaptive filter algorithms to 2D structures and the novel 2D adaptive filters are established. Based on this extension, the 2D selective partial update NLMS (2D-SPU-NLMS), the 2D selective partial update APA (2D-SPU-APA) and the 2D selective regressor APA (2D-SR-APA) are presented. In 2D-SPU adaptive algorithms, the filter coefficients are partially updated, and in 2D-SR-APA, the recent regressors of input signal are optimally selected in each time iteration. These algorithms reduce the computational complexity in 2D adaptive filter applications. In the following, a unified approach for the establishment and mean-square performance analysis of the family of 2D adaptive filter algorithms is presented. This analysis is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. We demonstrate the good performance of the proposed algorithms through several simulation results in 2D system identification and 2D adaptive noise cancellation (2D-ANC) for image restoration. The results are compared with the classical 2D adaptive filters such as 2D-LMS, 2D-NLMS, and 2D-APA. Also we show that the derived theoretical expressions are useful in predicting the steady-state and transient performance of 2D adaptive filter algorithms.

A unified approach to tracking performance analysis of the selective partial update adaptive filter algorithms in nonstationary environment

In this paper, a unified approach to mean-square performance analysis of the family of selective partial update (SPU) adaptive filter algorithms in nonstationary environment is presented. Using this analysis, the tracking performance of Max normalized least mean squares (Max-NLMS), N-Max NLMS, the various types of SPU-NLMS algorithms, SPU transform domain LMS (SPU-TD-LMS), the family of SPU affine projection algorithms (SPU-APA), the family of selective regressor APA (SR-APA), the dynamic selection of APA (DS-APA), the family of SPU-SR-APA, the family of SPU-DS-APA, SPU subband adaptive filters (SPU-SAF), and the periodic, sequential, and stochastic partial update LMS, NLMS, and APA as well as classical adaptive filter algorithms can be analyzed with a unified approach. Two theoretical expressions are introduced to study the performance. The analysis is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. We demonstrate through simulations that the derived expressions are useful in predicting the performance of this family of adaptive filters in nonstationary environment.

Adaptive filtering in phase space for foetal electrocardiogram estimation from an abdominal electrocardiogram signal and a thoracic electrocardiogram signal

In this study, an adaptive filtering in phase space for foetal electrocardiogram (ECG) extraction from a maternal abdominal ECG signal and a thoracic ECG signal is proposed. The phase space trajectory is reconstructed with one abdominal ECG signal and one thoracic ECG signal from a pregnant women. The non-linear and time-variant relationship between the thoracic ECG signal and maternal ECG component in abdominal signal is built by the adaptive local modelling in phase space, and the maternal ECG component in abdominal ECG signal can be estimated, which is then subtracted from the abdominal ECG signal to obtain a foetal ECG estimate. The local modelling process is carried on the spatial neighbouring points of each point in phase space, not on the temporal neighbouring points of each point in time sequence as usual. This can utilise the geometric information of the periodic ECG signal, and the simpler local relationship represented in phase space makes the estimation of the relationship easier and more accurate. Compared with the classical adaptive filter, the proposed method has shown the improved robustness and fidelity in restoration of the foetal ECG, testing with synthetic and real ECG signals.

SELF-OPTIMIZING REAL-VALUED GENERALIZED ADAPTIVE NOTCH FILTER

Generalized adaptive notch filters (GANFs) are used for identification/tracking of quasi-periodically varying dynamic systems and can be considered an extension, to the system case, of classical adaptive notch filters. The tracking properties of a GANF algorithm depend on two adaptation gains, which should be chosen so as to match the degree of nonstationarity of the identified system. First, an analytical study of a tracking performance of a real-valued GANF algorithm is presented. Then, based on the obtained theoretical results, a self-optimizing algorithm is proposed, capable of automatic tuning of its adaptation gains. The paper is an extension of the previous work devoted to complex-valued GANF algorithms.

Generalized Adaptive Notch Smoothers for Real-Valued Signals and Systems

Systems with quasi-periodically varying coefficients can be tracked using the algorithms known as generalized adaptive notch filters (GANFs). GANF algorithms can be considered an extension, to the system case, of classical adaptive notch filters (ANFs). We show that estimation accuracy of the existing algorithms, as well as their robustness to the choice of design parameters, can be considerably improved by means of compensating estimation delay effects which occur in the amplitude tracking and frequency tracking loops of GANF/ANF filters. Apart from the increased computational burden, the price for the achieved improvements is paid in terms of a decision delay-the proposed generalized adaptive notch smoothing (GANS) algorithms must be run on delayed input/output data sequences. Since such delay is acceptable in many signal processing and system identification applications, the proposed solution seems to be an attractive alternative to the currently available trackers.

Steady-state analysis of constrained normalized adaptive filters for MAI reduction by energy conservation arguments

We derive the steady-state performance of a common class of adaptive filters for multiple access interference (MAI) reduction in code division multiple access (CDMA) systems. The adaptive filters under study utilize estimates of the desired user's amplitude and are divided into three groups of least-mean-square (LMS) algorithms differing by the choice of the normalization factor. The steady-state performance is deduced from energy-conservation relations that include a possibly erroneous estimate of the desired user's amplitude. The analyses show that blind algorithms using information about the desired user's amplitude achieve similar performance to that of nonblind algorithms. In addition, geometric considerations reveal the conditions under which the choice of the normalization factor is expected to have great impact on the convergence properties of the algorithms. Numerical simulations show good agreement with theory.

On Tracking Properties of Real-Valued Generalized Adaptive Notch Filters

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Generalized adaptive notch filters (GANFs) are used for identification/tracking of quasi-periodically varying dynamic systems and can be considered an extension, to the system case, of classical adaptive notch filters. The paper presents results of local performance analysis of a real-valued GANF algorithm, i.e., algorithm designed to track parameters of a real-valued system. This is an extension of the previous work which focused on complex-valued systems. Tracking properties of the algorithm are studied analytically using an approximating linear filter technique and an averaging approach. Even though restricted to a single frequency case, the presented analysis provides interesting insights into the tracking mechanisms of GANF and allows one to formulate some useful rules of thumb for choosing design parameters. </para>

A unified framework for adaptive filter algorithms with variable step-size

Employing a recently introduced framework within which a large number of classical and modern adaptive filter algorithms can be viewed as special cases, we extend this framework to cover block normalized LMS (BNLMS) and normalized data reusing LMS (NDRLMS) adaptive filter algorithms. Accordingly, we develop a generic variable step-size adaptive filter. Variable step-size normalized LMS (VSSNLMS) and VSS affine projection algorithms (VSSAPA) are particular examples of adaptive algorithms covered by this generic variable step-size adaptive filter. In this paper we introduce two new VSS adaptive filter algorithms named the variable step-size BNLMS (VSSBNLMS) and the variable step-size NDRLMS (VSSNDRLMS) based on the generic VSS adaptive filter. The proposed algorithms show the higher convergence rate and lower steady-state mean square error compared to the ordinary BNLMS and NDRLMS algorithms.