Multichannel LMS Research Articles

Application of ground vibration testing in small manned or unmanned aircraft prototyping

The article describes the vibration measurement technology used in experimental investigation of light aircraft and some series of exemplary results obtained during the prototyping process. The aim of investigations presented herein was to determine the resonant frequencies and natural modes of an aircraft or its selected structural components. Ground vibration testing is an essential dynamic structural test necessary to carry out before the aircraft certification. This test should be performed on the aircraft example which is predicted to be tested in flight. The measuring system used for ground vibration testing in the Institute of Aviation Technology of the Military University of Technology consists of a multi-channel LMS SCADAS analyzer, a set of piezoelectric accelerometers, two vibration exciters equipped with impedance heads and a computer with the Test.Lab Software. The aim of the article was to present the methodology of performing ground vibration testing tests. Having applied the equipment to measure an airplane or its airframe component, key vibration characteristics corresponding to specific resonant points can be determined. Not only completed aircraft can be tested but also its isolated fragments (wings, stabilizers, tail units) or just empty airframe. Testing separately supported components allows examining their aeroelastic properties at early stage of prototyping. Ground vibration testing technology applied in various stages of the prototyping process was demonstrated in four peculiar research cases. The testing examples presented herein were the following: the isolated strut-braced wing of a light reconnaissance airplane, the light drone imitating an aerial target for some on-ground anti-aircraft artillery sets, the empty airframe of a very light jet and the miniature UAV. Some exemplary results obtained from testing these objects were presented. At the end, some observations and conclusions were noted in the context of usefulness of conducted researches.

Technology of Ground Vibration Testing and its application in light aircraft prototyping

The article describes the vibration measurement technology used for light aircraft and some results obtained during the prototyping process. The aim of researches was to determine the resonant frequencies and natural modes of an aircraft or its selected structural components. Ground Vibration Testing is an essential dynamic structural test necessary to carry out before the aircraft certification. This test should be performed on the aircraft example which is predicted to test in flight. The measuring system used in the Institute of Aviation Technology of the Military University of Technology consists of a multi-channel LMS SCADAS analyzer, a set of piezoelectric accelerometers, two vibration exciters equipped with impedance heads and a computer with the Test.Lab Software. The aim of the article is to present the methodology of performing GVT tests. Using the equipment applied to an aircraft or its airframe component, key vibration characteristics corresponding to resonant points can be determined. Not only completed aircraft can be tested, but also its isolated fragments (wings, stabilizers, tail units) or just empty airframe. Testing separately supported components allows to examine their aeroelastic properties at early stage of prototyping. As examples of the use of vibration measurements in various stages of the prototyping process, three examples are presented herein. The isolated strut-braced wing from of the light reconnaissance aircraft OSA, the airframe of a light jet FLARIS LAR, and the light drone ATD JET-2 intended to be an aerial target for some anti-aircraft artillery sets. Some exemplary results obtained from testing these objects were presented. At the end, some observations and conclusions were noted in the context of usefulness of conducted researches.

Experimental verification of the numerical model of a reinforced concrete arch

The paper presents numerical and experimental dynamic analysis of the reinforced concrete arch. The measurement of the accelerations at various points of the structure as a response to the force (single input-multiple output approach) was made. Dynamic response of the concrete arch has been measured by piezoelectric accelerometers and signals have been recorded by multi-channel LMS SCADAS Mobile recorder. As a result of the measurements frequencies and mode shapes have been determined. ADINA program has been used for building of numerical models. The static scheme of the arch (the stiffness of the supports) has been updated using the genetic algorithms (GA) available in MATLAB to obtain the results, which correspond to dynamic testing. Two similarity criteria have been considered: the mean squared error (MSE) between frequencies and the Modal Assurance Criterion (MAC) for forms comparison. In additional example, the Young’s modulus has been also optimized.

HASAN: Highly accurate sensitivity for auto-contrast-corrected pMRI reconstruction

A novel method for highly accurate coil sensitivity-map estimation, based on a constrained image-domain multi-channel LMS (c-iMCLMS) algorithm, is proposed for image reconstruction using self-calibrating SENSE. The sensitivity information is extracted by developing an image-domain cross-relation equation using the low-resolution images constructed from the fully sampled central region of the variable density MR data. Then this formulation is solved in an iterative way using a novel sum-of-squares (SOS) constraint. The improvement of the convergence speed of the c-iMCLMS algorithm is accomplished by SOS normalization of the low resolution image data and using a variable step-size in the update equation. The salient feature of the proposed technique is that it does not require any prior selection of the basis function and/or simultaneous estimation of the object image and the coil sensitivity-map. Only the low resolution images are re-filtered for the compensation of the data truncation effect to improve the consistency of the estimated coil maps. Besides, the application of the novel SOS-constraint, estimated using the pixel position-wise variance of the coil maps, gives closest to the true sensitivity-map. As a result, true object image with auto-corrected contrast is reconstructed without adopting any traditional post-contrast correction techniques. For minimization of the process noise, regularized conjugate gradient (CG) based SENSE reconstruction algorithm is used for image reconstruction using the estimated coil sensitivity-map. The proposed technique is tested on various simulation, synthetic and in-vivo datasets and significant signal-to-artifact-noise-ratio (SANR) improvement closest to the theoretical limit set by coil geometric factor is obtained as compared to some noted techniques in the literature both visually and numerically.

Generalized Robust Multichannel Frequency-Domain LMS Algorithms for Blind Channel Identification

Recently, several noise-robust adaptive multichannel LMS algorithms have been proposed based on the spectral flatness of the estimated channel coefficients in the presence of additive noise. In this work, we propose a general form for the algorithms that integrates the existing algorithms into a common framework. Computer simulation results are presented and demonstrate that a new proposed algorithm gives better performance compared to existing algorithms in noisy environments.

Generalized Robust Multichannel Frequency-Domain LMS Algorithms for Blind Channel Identification

Recently, several noise-robust adaptive multichannel LMS algorithms have been proposed based on the spectral flatness of the estimated channel coefficients in the presence of additive noise. In this work, we propose a general form for the algorithms that integrates the existing algorithms into a common framework. Computer simulation results are presented and demonstrate that a new proposed algorithm gives better performance compared to existing algorithms in noisy environments.

Dereverberation and denoising based on generalized spectral subtraction by multi-channel LMS algorithm using a small-scale microphone array

A blind dereverberation method based on power spectral subtraction (SS) using a multi-channel least mean squares algorithm was previously proposed to suppress the reverberant speech without additive noise. The results of isolated word speech recognition experiments showed that this method achieved significant improvements over conventional cepstral mean normalization (CMN) in a reverberant environment. In this paper, we propose a blind dereverberation method based on generalized spectral subtraction (GSS), which has been shown to be effective for noise reduction, instead of power SS. Furthermore, we extend the missing feature theory (MFT), which was initially proposed to enhance the robustness of additive noise, to dereverberation. A one-stage dereverberation and denoising method based on GSS is presented to simultaneously suppress both the additive noise and nonstationary multiplicative noise (reverberation). The proposed dereverberation method based on GSS with MFT is evaluated on a large vocabulary continuous speech recognition task. When the additive noise was absent, the dereverberation method based on GSS with MFT using only 2 microphones achieves a relative word error reduction rate of 11.4 and 32.6% compared to the dereverberation method based on power SS and the conventional CMN, respectively. For the reverberant and noisy speech, the dereverberation and denoising method based on GSS achieves a relative word error reduction rate of 12.8% compared to the conventional CMN with GSS-based additive noise reduction method. We also analyze the effective factors of the compensation parameter estimation for the dereverberation method based on SS, such as the number of channels (the number of microphones), the length of reverberation to be suppressed, and the length of the utterance used for parameter estimation. The experimental results showed that the SS-based method is robust in a variety of reverberant environments for both isolated and continuous speech recognition and under various parameter estimation conditions.

Distant-Talking Speech Recognition Based on Spectral Subtraction by Multi-Channel LMS Algorithm

SUMMARY We propose a blind dereverberation method based on spectral subtraction using a multi-channel least mean squares (MCLMS) algorithm for distant-talking speech recognition. In a distant-talking environment, the channel impulse response is longer than the short-term spectral analysis window. By treating the late reverberation as additive noise, a noise reduction technique based on spectral subtraction was proposed to estimate the power spectrum of the clean speech using power spectra of the distorted speech and the unknown impulse responses. To estimate the power spectra of the impulse responses, a variable step-size unconstrained MCLMS (VSS-UMCLMS) algorithm for identifying the impulse responses in a time domain is extended to a frequency domain. To reduce the effect of the estimation error of the channel impulse response, we normalize the early reverberation by cepstral mean normalization (CMN) instead of spectral subtraction using the estimated impulse response. Furthermore, our proposed method is combined with conventional delay-andsum beamforming. We conducted recognition experiments on a distorted speech signal simulated by convolving multi-channel impulse responses with clean speech. The proposed method achieved a relative error reduction rate of 22.4% in relation to conventional CMN. By combining the proposed method with beamforming, a relative error reduction rate of 24.5% in relation to the conventional CMN with beamforming was achieved using only an isolated word (with duration of about 0.6s) to estimate the spectrum of the impulse response.

The Quaternion LMS Algorithm for Adaptive Filtering of Hypercomplex Processes

The quaternion least mean square (QLMS) algorithm is introduced for adaptive filtering of three- and four-dimensional processes, such as those observed in atmospheric modeling (wind, vector fields). These processes exhibit complex nonlinear dynamics and coupling between the dimensions, which make their component-wise processing by multiple univariate LMS, bivariate complex LMS (CLMS), or multichannel LMS (MLMS) algorithms inadequate. The QLMS accounts for these problems naturally, as it is derived directly in the quaternion domain. The analysis shows that QLMS operates inherently based on the so called ldquoaugmentedrdquo statistics, that is, both the covariance <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</i> { <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xx</b> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</sup> } and pseudocovariance <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</i> { <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xx</b> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> } of the tap input vector <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</b> are taken into account. In addition, the operation in the quaternion domain facilitates fusion of heterogeneous data sources, for instance, the three vector dimensions of the wind field and air temperature. Simulations on both benchmark and real world data support the approach.

Fast exact multichannel FSLMS algorithm for active noise control

In this paper, we derive the fast and exact implementation of multichannel filtered-S LMS (FSLMS) algorithm. By removing redundancy in the governing equations of the standard FSLMS algorithm, we can obtain a significant reduction in the complexity of the controller. This has been achieved by rearranging the equations of FSLMS algorithm. We have carried out a detailed computational complexity analysis. Simulation experiments have been carried out to show the performance equivalence of the proposed fast algorithm with its standard version.

Variable step-size multichannel frequency-domain LMS algorithm for blind identification of finite impulse response systems

The choice of step size is a critical factor in blind identification of single-input multi-output systems using adaptive multichannel frequency-domain least-mean-squares (MCFLMS) algorithm. The proper step size is dependent on the signal power and it influences the speed of convergence and the final misalignment error. Applying normalised MCFLMS (NMCFLMS) algorithm, the dependency of the step-size parameter on signal power can be eliminated; however, it is shown that even for a moderate signal-to-noise ratio, the algorithm fails to converge to the eigenvector corresponding to the minimum eigenvalue of the data correlation matrix and hence misconverges to a fictitious solution. A self-adaptive variable step-size MCFLMS (VSS-MCFLMS) algorithm that optimises the performance of the algorithm in each iteration in order to achieve minimum misalignment with the true channel impulse response is proposed. The convergence analysis of the proposed algorithm is performed and it is shown that the VSS-MCFLMS algorithm converges, both in noise-free and noisy conditions, to the eigenvector corresponding to the minimum eigenvalue and is therefore more noise robust when compared with the NMCFLMS. The algorithm also shows the optimal speed of convergence in a noise-free case and the proposed variable step size guarantees the stability of the algorithm. Simulation results are presented to demonstrate that the proposed VSS-MCFLMS algorithm gives lower final misalignment when compared with the NMCFLMS algorithm without sacrificing the speed of convergence and computational efficiency.

Preconditioning multichannel adaptive filtering algorithms using EVD- and SVD-based signal prewhitening and system decoupling

It is well known that the convergence rate of multichannel LMS-based algorithms is limited by the correlation properties of the reference signals and the cross-coupling within the plant dynamics. These factors give rise to excessive eigenvalue spread and slow convergence rate of a gradient descent algorithm. A preconditioning technique is developed in this study for the multichannel LMS algorithm so as to improve its convergence rate. Signal prewhitening and system decoupling are the two key elements of the proposed techniques. Preconditioning filters are first formulated in the frequency domain by using eigenvalue decomposition and singular value decomposition. These filters are then transformed into the time domain with causality taken into account. The preconditioning filters are incorporated into a multichannel LMS algorithm, where the reference signals are prewhitened and the plants are decoupled prior to the adaptation process. Simulations for a two-channel/one listener cross-talk cancellation problem illustrate the effectiveness of the preconditioning technique in improving the convergence rate of the adaptive algorithms.

A class of frequency-domain adaptive approaches to blind multichannel identification

We extend our previous studies on adaptive blind channel identification from the time domain into the frequency domain. A class of frequency-domain adaptive approaches, including the multichannel frequency-domain LMS (MCFLMS) and constrained/unconstrained normalized multichannel frequency-domain LMS (NMCFLMS) algorithms, are proposed. By utilizing the fast Fourier transform (FFT) and overlap-save techniques, the convolution and correlation operations that are computationally intensive when performed by the time-domain multichannel LMS (MCLMS) or multichannel Newton (MCN) methods are efficiently implemented in the frequency domain, and the MCFLMS is rigorously derived. In order to achieve independent and uniform convergence for each filter coefficient and, therefore, accelerate the overall convergence, the coefficient updates are properly normalized at each iteration, and the NMCFLMS algorithms are developed. Simulations show that the frequency-domain adaptive approaches perform as well as or better than their time-domain counterparts and the cross-relation (CR) batch method in most practical cases. It is remarkable that for a three-channel acoustic system with long impulse responses (256 taps in each channel) excited by a male speech signal, only the proposed NMCFLMS algorithm succeeds in determining a reasonably accurate channel estimate, which is good enough for applications such as time delay estimation.

ROBUST DESIGN AND ROBUST STABILITY ANALYSIS OF ACTIVE NOISE CONTROL SYSTEMS

The problem of designing robust active control systems is addressed in this paper. A variety of active control design problems are formulated as semidefinite programming (SDP) problems. An SDP problem is a convex optimization problem, consisting of a linear objective function subject to linear matrix inequality (LMI) constraints. First, an SDP formulation is presented for the design of multichannel LMS algorithms with limited-capacity secondary sources. Simulations show that this SDP formulation is an order of magnitude more computationally efficient than the usual non-linear constrained optimization formulations. Secondly, the design of robust LMS algorithms is presented as an SDP problem. These algorithms minimize the worst-case control error in the presence of unknown but norm-bounded perturbations on the secondary path model and on the primary field. Both the unstructured and structured perturbations cases are considered. The resulting controllers are exact solutions to the robust control design problem, except in the most general case of structured perturbations when they only minimize an upper bound on the worst-case residual control error. Thirdly, SDP formulations are proposed to compute guaranteed stability limits for the adaptive multiple-channel leaky LMS algorithm in the presence of both unstructured and structured perturbations on the secondary path. Monte Carlo simulations show that the obtained stability limits are much more reliable than previously used limits, based, for example, on the Gershgorin circle theorem.

Active control of spinning mode noise from a turbofan engine

A JT15D turbofan engine is mounted in a test cell and operated to produce a fan blade passage frequency (BPF) of 2300 Hz. Twenty-seven flow disturbing rods are placed upstream of the 28 fan blades exciting to dominance the first circumferential spinning mode. A feedforward filtered-x adaptive multichannel LMS algorithm is implemented into a TMS320C30 DSP board. This three-channel controller is used to attenuate both the BPF tone and the first harmonic of the BPF (2BPF) tone components of the fan noise. The BPF and 2BPF frequency are sensed with a magnetic probe in the casing of the fan. This reference signal is an input to three adaptive finite-impulse response filters and whose outputs are fed forward to an array of 12 acoustic drivers mounted around the circumference of the engine inlet. The 12 drivers are divided into three channels of four drivers each. The far-field acoustic error information is obtained from three polyvinylidene fluoride piezoelectric polymer (PVDF) microphones placed in front of the engine. Experiments have demonstrated reductions of up to 10 dB in significant sectors. [Research supported by NASA Langley Research Center.]

Effect of errors in the plant model on the performance of algorithms for adaptive feedforward control

The paper presents an investigation into the effects of errors in the assumed plant model on the performance of both single and multichannel adaptive algorithms for the active control of sound and vibration. In the single-channel case the effects of phase errors on the maximum update coefficient and convergence time of the filtered-x LMS algorithm are considered. This involves both analytic results and computer simulations. For the multichannel case a statistical analysis is presented examining the effects of a Gaussian distribution of errors in the assumed plant response on the convergence of the multichannel LMS algorithm. The work includes experimental results from a 32 microphone, 16 loudspeaker active noise control system.

Algorithm for multichannel LMS adaptive filtering

A modification of the LMS algorithm is presented in which the coefficients of an array of FIR filters, whose outputs are linearly coupled to an array of sensors, are adapted to minimise the mean-square sum of the error signals from these sensors. The application of the algorithm to the control of distributed parameter systems is discussed.