Multichannel Adaptive Filtering Research Articles

An adaptive constrained multi-channel active noise control filter design approach using convex cone optimization

In current practical active noise control (ANC) applications, solutions to three main technical challenges need to be sought: the requirement of a stable controller when acoustic feedback paths exist, a larger quiet zone, and adaptation to a time-varying environment. The imposed controller stability constraints can significantly increase the computational requirements and negatively impact the adaptation of ANC filters in real time. Multi-channel systems are usually needed if a larger quiet zone is required while the coupling between different channels can greatly lower the convergence rate of adaptive algorithms. In the current work, a multi-channel adaptive constrained ANC filter design method is proposed. The recently proposed convex cone formulation for ANC design is adopted to reduce the computational time of solving the constrained ANC filter design problem. The optimal filter coefficients are then redesigned continuously using convex optimization when the environment is time-varying. The experimental result shows that, compared with the traditional leaky FxLMS method, the proposed method has a faster convergence rate and a better steady-state noise control performance.

Design of multichannel adaptive filter by constructing multidimensional Wiener-Hopf equation

In the satellite-to-ground high-speed data transmission link, there are signal self-interference problems of symbols in the co-channel, as well as between orthogonal and polarized channels. A multichannel adaptive filter is designed by constructing a multichannel Wiener-Hopf equation, and the influence of five channel nonideal factors is suppressed to improve the BER performance. Experiments show that this method is effective to suppress the signal self-interference, and the BER floor is optimized from 1E-3 to 1E-7.

GMA phased array for active echo control of underwater target

Aiming at the special requirements of low-frequency echo control of underwater targets in high pressure environment, a low-frequency miniaturized active control unit with thickness less than 50 mm was designed based on Terfenol-D, a giant magnetostrictive material. Based on that, a kind of light and thin underwater large plate smart acoustic structure was constructed. It was used for low-frequency active echo control of underwater targets. The active control unit was constructed into a flat smart acoustic structure using a 4 × 4 array distributed design. The active control system based on multi-channel adaptive filter was used to test the echo control performance of the flat active acoustic structure, which can achieve significant noise reduction level in less than one second. And the sound absorption coefficient of 0.8 or more can be achieved at a high pressure of 1 MPa. When the sound wave was incident on the surface of the underwater target obliquely from −10° to +10°, the phased array method was used to make the pointing angle of the GMA array consistent with the incident angle, which could control the surface echo of the active structure very well, and the sound absorption coefficient is close to 0.9. The giant magnetostrictive smart structure combined with the active control system is a new type of low-frequency underwater noise reduction technology.

Combined adaptive antenna array and adaptive channel equalizer, based on multichannel fast recursive least squares algorithms

An adaptive antenna array (AAA) is a sort of directional antennas, which is able to suppress the signals of the interference sources. The AAA works well if there is no multipath propagation of the information signal. A device, which removes the multipath effects, is an adaptive equalizer. However, the independent AAA and equalizer do not work well if the interferences and multipath exist simultaneously. The purpose of this paper is to present a new efficient solution of the task by using of the AAA, whose weights are shared with the weights of the fractionally-spaced feed-forward (FF) part of the equalizer. The array output signal is combined with that of the feed-backward (FB) part of the equalizer. The array and such distributed equalizer operate as an indivisible adaptive filter, providing good performance and ability to receive the multipath information signals in the presence of the signals of the external interference sources. Because the FF part of the equalizer operates at the up-sampled rate, which is a few times higher than the information symbols rate, but the FB filter operates at the symbol rate, the computationally efficient fast recursive least squares (RLS) algorithms cannot be used directly in the considered adaptive unit. To solve this problem, it is proposed to use polyphase representation of the FF part of the equalizer. In this case, the architecture of the adaptive unit is viewed as a multichannel adaptive filter with unequal number of weights in channels. The paper presents the architecture of the adaptive unit and the details of its equalizer part. The mathematical description of a multichannel fast adaptive filtering algorithm, called stabilized fast a posteriori sequential technique (FAEST), fitted for the weights calculation of the considered unit is also presented. The verification of the proposed adaptive unit has been carried out via simulation, using a linear array with sixteen omnidirectional antennas, which operates in the conditions of –30 dB signal-to-interference ratio (SIR), 10…30 dB signal-to-noise ratio (SNR), receives the phase shift keying (8-PSK) desired signal, passed through a two-rays communication channel with about –65 dB gaps in the channel amplitude-frequency response. In the steady-state, the proposed adaptive unit demonstrates about –100 dB radiation pattern gaps towards the interference sources and the transient response with less than 500 symbols duration. The simulation demonstrates the ability of the proposed unit simultaneously to steer the main lobe of antenna array radiation pattern towards the desired signal source without a priori search of its angular location and to remove the signals of the external interferences and intersymbol interference in the array output signal. Due to these properties, the proposed AAA/equalizer can be used in equipment of modern mobile communication systems.

Adaptive Processing for EM Telemetry Signal Recovery: Field Data from Sichuan Province

This paper deals with the study of multi-channel adaptive noise cancellation with a focus on its application in electromagnetic (EM) telemetry. We presented new variable step-size least mean square (LMS) techniques: regularized variable step-size LMS and regularized sigmoid variable size LMS, for electromagnetic telemetry data processing. Considering the complexity and spatial distribution of environmental noise, algorithms with multiple reference signals were used to retrieve transmitted EM signals. The feasibility of the regularized variable step size LMS algorithms with numerical simulation was analyzed and presented. The adaptive processing techniques were applied in the recovery of frequency and binary phase shift key modulated signal. The proposed multi-channel adaptive technique achieves fast convergence speed, low mean squared error and is shown to have good convergence characteristics compared to conventional methods. In addition to attaining good results from the multi-channel adaptive filter and performing the signal analysis in real-time, we implemented combined fast effective impulse noise removal techniques. The combination of median and mean filters was effective in removing a wide range of impulsive noises without distorting any other data points. Further, electromagnetic telemetry data were acquired during a drilling operation in Sichuan province, China, for real field application. Data processing workflow was designed for EM telemetry data processing based on the noise characteristics, simulation results and expected result for demodulation. To establish a comprehensive overview, a performance comparison of the acquisition array system is also provided. Conclusively, the introduced multichannel adaptive noise canceling techniques are very effective in recovering transmitted EM telemetry signals.

Sea Clutter Cancellation for Passive Radar Sensor Exploiting Multi-Channel Adaptive Filters

Sea clutter suppression in passive radar sensor is a challenging problem because the Doppler frequencies of low-velocity sea-surface targets are typically close to the spectrum of the sea clutter. Conventional approaches based on single-channel high-pass filters are effective for clutter suppression only when the clutter is concentrated in low Doppler region. For sea clutter that has a spread spectrum, however, these approaches have to compromise target signal reception. That is, they either form a narrow notch which does not effectively suppress clutter, or generate a broadened null that simultaneously mitigates low-velocity target signals. Therefore, it is desirable to design a filter that forms a notch broad enough to cover the entire clutter spectrum, with the frequency response rising sharply to a high gain outside the clutter band. Toward this end, in this paper, we develop a generalized multi-channel adaptive filter which, by forming multiple sharp notches over a set of discrete frequencies within the clutter spectrum, achieves effective clutter suppression and target signal preservation. We focus on the fast frequency-domain implementation, and the performance analysis in terms of the frequency response, signal energy loss, and computational complexities is also presented. The effectiveness of the proposed approaches is verified using real-data results.

Space-time multichannel adaptive filtering scheme for VLC color cross-talk equalization.

In the emerging framework of visible light communications, the limitation of modulation bandwidth of light emitting diodes (LEDs) transmitters is counterbalanced by the use of red-green-blue (RGB) LEDs since they exhibit higher modulation bandwidth. Using RGB LEDs allows to exploit the spatial multiplexing aspects, that is obtaining parallel channels for transmission. In this context, we used at the receiver RGB-tuned photodiodes (PDs). Hence each PD can, in principle, detect only the information its tuning is matched to. The drawback of increasing spectral efficiency is the presence of cross-talk at the single PD due to the signals emitted by the remaining colors when a perfect color filtering is not applied. In this paper we consider amplitude shift keying (ASK) modulation, investigate the issue of the spatial (color) interference and we present a blind multichannel adaptive equalizer able to mitigate the temporary intersymbol interference (ISI) and also the spatial (color) ISI, this latter partially suppressed using suitable color filters. We analyze also the convergence of the adaptive algorithm and compare its performance with the literature.

MIMO beamforming system for speech enhancement in realistic environment with multiple noise sources

Multiple noise sources in a realistic environment severely degrade the quality and intelligibility of the desired speech signal, thus posing a severe problem for many speech applications. Several noise reduction algorithms have been proposed with a main goal to solve this problem. However, the good performances of such algorithms are severely impaired in realistic environment under multi-noise sources condition. In this paper, the author treats the noise cancellation system as a multiple-input multiple-output (MIMO) beamformer system. The proposed approach consists of two steps. First, the noise signals are generated by applying the white noise sources to a MIMO AR system. Then, the noisy microphone signals are sequentially processed by employing multi-channel linear prediction error filters (MCLPEFs) and multi-channel adaptive noise estimation filters (MCANEFs) in the lower path of the proposed beamformer. The MCLPEFs are used to whiten the input signals, while the MCANEFs are used as a MIMO system identification to perform the modeling process of the noise signals. Finally, the noise signals in the upper path are subtracted from the estimated noises in the lower path to recover an enhanced speech signal. Moreover, the performance of the proposed MIMO approach was validated under a realistic environment with real noise sources.

Nonlinear Acoustic Echo Cancellation Based on Multichannel Adaptive Filters: A Novel Approach

This paper presented a new class of non-linear adaptive filters, whose structure is based on Hammerstein model. Such filters designed from the functional link adaptive filter (FLAF) model, described by a non-linear input expansion which develops the illustration of the input signal through a projection in a higher dimensional space and a subsequent adaptive filtering. In order to give toughness against different degrees of non-linearity, a collaborative FLAF is proposed based on the adaptive combination of filters. Such architecture provided the best performance regardless of the nonlinearity degree in the echo path. Particularly this architecture can accomplish the linear and non-linear filters. Such as Normalized least mean square algorithm using FDAF, Affine projection and Lattice filters are used. When comparing their Echo return loss enhancement performance, Affine projection algorithm gives 20 dB more than others in this work.

Suppressing multiples using an adaptive multichannel filter based on L1-norm

Adaptive subtraction is an important link for removing surface-related multiples in the wave equation-based method. In this paper, we propose an adaptive multichannel subtraction method based on the L1-norm. We achieve enhanced compensation for the mismatch between the input seismogram and the predicted multiples in terms of the amplitude, phase, frequency band, and travel time. Unlike the conventional L2-norm, the proposed method does not rely on the assumption that the primary and the multiples are orthogonal, and also takes advantage of the fact that the L1-norm is more robust when dealing with outliers. In addition, we propose a frequency band extension via modulation to reconstruct the high frequencies to compensate for the frequency misalignment. We present a parallel computing scheme to accelerate the subtraction algorithm on graphic processing units (GPUs), which significantly reduces the computational cost. The synthetic and field seismic data tests show that the proposed method effectively suppresses the multiples.

Low-frequency active noise control of an underwater large-scale structure with distributed giant magnetostrictive actuators

A light and thin underwater large-plate active acoustic structure is developed that satisfies the particular requirements of high pressure resilience, low frequency and high efficiency encountered in underwater work environments. A low-frequency miniaturized active control unit, with a thickness of less than 50mm, is designed using giant magnetostrictive material (GMM). The noise reduction performance is measured with an active control system based on a multi-channel adaptive filter. The active control system is developed within a LabVIEW environment and can achieve significant levels of noise reduction within time intervals of less than one second achieving absorption coefficients far exceeding 0.8 even under high pressures. The new active-control system incorporates hardware and software components and represents a novel technology for low-frequency underwater noise reduction.

Анализ систем обработки сигналов на фоне пассивных помех

An adaptive quasi-optimal "band-stop filter – multichannel filter" system is analyzed. Adaptation of such system opens the possibility of taking into account the nature of noise represented by an additive mixture of correlated and uncorrelated components corresponding to clutter and the receiver’s own noise. A likelihood function establishing the dependence of the processing system input samples on the clutter correlation matrix is introduced, using which the accuracy of estimating the unknown clutter parameters, namely, the interperiod correlation coefficients and the Doppler phase shift can be analyzed. Formulas establishing a relation between the accuracy of estimating the unknown clutter parameters, the amount of training sample, and spectral correlation properties of the clutter are obtained proceeding from the Cramer-Rao bound. The calculation results were found to be in fairly close agreement with the empirical results obtained from statistical simulation of the relevant evaluation algorithms on a PC; this agreement has confirmed the asymptotic efficiency of the maximum likelihood estimates used in adapting the processing systems. The processing system efficiency analysis carried out by using the asymptotic properties of the maximum likelihood estimates establishes a relationship between the suppression of clutter in the system and the errors of adapting the weighting coefficients of the adaptive band-stop and multi-channel filters to the unknown clutter parameters. In so doing, a linear approximation of the dependencies of weighting coefficients from the interperiod correlation coefficient estimate was used under the assumption that the form of the clutter correlation function’s envelope is known. The relationships obtained from the performed analysis allow the user to select the training sample amount depending on the preset adaptation loss value, spectral correlation properties of clutter, and processing system structure.

The impact of head movements on EEG and contact impedance: an adaptive filtering solution for motion artifact reduction.

Designing and developing a comfortable and convenient EEG system for daily usage that can provide reliable and robust EEG signal, encompasses a number of challenges. Among them, the most ambitious is the reduction of artifacts due to body movements. This paper studies the effect of head movement artifacts on the EEG signal and on the dry electrode-tissue impedance (ETI), monitored continuously using the imec's wireless EEG headset. We have shown that motion artifacts have huge impact on the EEG spectral content in the frequency range lower than 20 Hz. Coherence and spectral analysis revealed that ETI is not capable of describing disturbances at very low frequencies (below 2 Hz). Therefore, we devised a motion artifact reduction (MAR) method that uses a combination of a band-pass filtering and multi-channel adaptive filtering (AF), suitable for real-time MAR. This method was capable of substantially reducing artifacts produced by head movements.

Short-term spatio-temporal prediction of wind speed and direction

This paper aims to produce a low-complexity predictor for the hourly mean wind speed and direction from 1 to 6 h ahead at multiple sites distributed around the UK. The wind speed and direction are modelled via the magnitude and phase of a complex-valued time series. A multichannel adaptive filter is set to predict this signal on the basis of its past values and the spatio-temporal correlation between wind signals measured at numerous geographical locations. The filter coefficients are determined by minimizing the mean square prediction error. To account for the time-varying nature of the wind data and the underlying system, we propose a cyclo-stationary Wiener solution, which is shown to produce an accurate predictor. An iterative solution, which provides lower computational complexity, increased robustness towards ill-conditioning of the data covariance matrices and the ability to track time-variations in the underlying system, is also presented. The approaches are tested on wind speed and direction data measured at various sites across the UK. Results show that the proposed techniques are able to predict wind speed as accurately as state-of-the-art wind speed forecasting benchmarks while simultaneously providing valuable directional information.

Signal Quality Estimation With Multichannel Adaptive Filtering in Intensive Care Settings

A signal quality estimate of a physiological waveform can be an important initial step for automated processing of real-world data. This paper presents a new generic point-by-point signal quality index (SQI) based on adaptive multichannel prediction that does not rely on ad hoc morphological feature extraction from the target waveform. An application of this new SQI to photoplethysmograms (PPG), arterial blood pressure (ABP) measurements, and ECG showed that the SQI is monotonically related to signal-to-noise ratio (simulated by adding white Gaussian noise) and to subjective human quality assessment of 1361 multichannel waveform epochs. A receiver-operating-characteristic (ROC) curve analysis, with the human "bad" quality label as positive and the "good" quality label as negative, yielded areas under the ROC curve of 0.86 (PPG), 0.82 (ABP), and 0.68 (ECG).

Adaptive spatio-temporal filtering of disturbed ECGs: a multi-channel approach to heartbeat detection in smart clothing

Intermittent disturbances are common in ECG signals recorded with smart clothing: this is mainly because of displacement of the electrodes over the skin. We evaluated a novel adaptive method for spatio-temporal filtering for heartbeat detection in noisy multi-channel ECGs including short signal interruptions in single channels. Using multi-channel database recordings (12-channel ECGs from 10 healthy subjects), the results showed that multi-channel spatio-temporal filtering outperformed regular independent component analysis. We also recorded seven channels of ECG using a T-shirt with textile electrodes. Ten healthy subjects performed different sequences during a 10-min recording: resting, standing, flexing breast muscles, walking and pushups. Using adaptive multi-channel filtering, the sensitivity and precision was above 97% in nine subjects. Adaptive multi-channel spatio-temporal filtering can be used to detect heartbeats in ECGs with high noise levels. One application is heartbeat detection in noisy ECG recordings obtained by integrated textile electrodes in smart clothing.

Frequency-domain criterion for the speech distortion weighted multichannel Wiener filter for robust noise reduction

Recently, a generalized multi-microphone noise reduction scheme, referred to as the spatially pre-processed speech distortion weighted multichannel Wiener filter (SP-SDW-MWF), has been presented. This scheme consists of a fixed spatial pre-processor and a multichannel adaptive noise canceler (ANC) optimizing the SDW-MWF cost function. By taking speech distortion explicitly into account in the design criterion of the multichannel ANC, the SP-SDW-MWF adds robustness to the standard generalized sidelobe canceler (GSC). In this paper, we present a multichannel frequency-domain criterion for the SDW-MWF, from which several – existing and novel – adaptive frequency-domain algorithms can be derived. The main difference between these adaptive algorithms consists in the calculation of the step size matrix (constrained vs. unconstrained, block-structured vs. diagonal) used in the update formula for the multichannel adaptive filter. We investigate the noise reduction performance, the robustness and the tracking performance of these adaptive algorithms, using a perfect voice activity detection (VAD) mechanism and using an energy-based VAD. Using experimental results with a small-sized microphone array in a hearing aid, it is shown that the SP-SDW-MWF is more robust against signal model errors than the GSC, and that the block-structured step size matrix gives rise to a faster convergence and a better tracking performance than the diagonal step size matrix, only at a slightly higher computational cost.

Multichannel parallelizable sliding window RLS and fast RLS algorithms with linear constraints

This paper presents new sliding window (SW) recursive least squares (RLS) and fast RLS algorithms for adaptive filtering with linear constraints. The algorithms are formulated for the general case of multichannel adaptive filters with complex-valued weights, and are based on Papaodysseus's matrix inversion lemma. The lemma is used for SW correlation matrix inversion and for the inversion of some other matrices that appear in constrained SW RLS algorithms. The algorithms have a form that can be implemented by means of at least two parallel processors. The proposed algorithms can be used for time-domain adaptive filtering of non-stationary signals, whilst restricting the frequency response of the filter to specific values at particular frequencies. In addition, the algorithms can be used in linearly constrained adaptive beamforming, dealing with non-stationary interference.

A Statistically-Switched Adaptive Vector Median Filter

This paper presents a new cost-effective, adaptive multichannel filter taking advantage of switching schemes, robust order-statistic theory and approximation of the multivariate dispersion. Introducing the statistical control of the switching between the vector median and the identity operation, the developed filter enhances the detail-preserving capability of the standard vector median filter. The analysis and experimental results reported in this paper indicate that the proposed method is capable of detecting and removing impulsive noise in multichannel images. At the same time, the method is computationally efficient and provides excellent balance between the noise attenuation and signal-detail preservation. Excellent performance of the proposed method is tested using standard test color images as well as real images related to emerging virtual restoration of artworks.

Practical implementation of multichannel adaptive filters based on FTF and AP algorithms for active control

AbstractIn this paper, multichannel affine projection (AP) algorithms and fast transversal filters (FTF) are introduced for active noise control. A comparative practical study of the mentioned algorithms with the filtered‐X LMS (F‐XLMS) and the recursive least squares (RLS) is presented for multichannel systems. This study is based on simulations using real data and is mainly focused on: their computational cost and memory load, their convergence properties, their stability and their ability to create quiet zones around listener ears. Simulations show that algorithms based on FTF exhibit a good trade‐off between computational cost and convergence speed. On the other hand, those based on RLS are slightly faster but they present higher computational load and stability problems in their practical implementation. It has also been observed that algorithms based on low order AP algorithms present less computational cost than the FTF‐based ones but a slightly slower convergence speed. Therefore these algorithms show a desirable behaviour and versatility for practical applications. Finally, results obtained in a real‐time multichannel system validate the use of AP algorithms in practical applications as an alternative to the classical multichannel F‐XLMS since they provide meaningful attenuation levels, lower convergence time and similar computational cost. Additionally, as simulations indicated, AP algorithm performance can be easily improved increasing its projection order and using fast versions. Copyright © 2004 John Wiley & Sons, Ltd.