Desired Response Signal Research Articles

Integration of Health Monitoring and Control of Building Structures during Earthquakes

AbstractA hybrid real-time structural health monitoring and control system for building structures is presented in this study. A model-reference adaptive control algorithm for the designed substructures was developed and integrated with a previously developed interstory drift–based acceleration feedback method for health monitoring. A virtual healthy model, installed with health monitors, is used to generate proper control forces to obtain the desired response for the controlled substructure during a disastrous event such as an earthquake. An adaptive controller and an adaptation mechanism are designed using the Lyapunov theory to calculate the real-time adaptive control force. The local feedback control actuates the actual substructures to track the desired response signals. The results obtained by numerical simulations for the illustrative example in this study are further validated by experimental investigations using a 3-story aluminum frame structure. The asymptotical tracking of the state of the sub...

Study on Road Simulation Test of Motorcycle

The principle of remote parameter control(RPC) and test method of road simulator test are analyzed, and the road simulation test of motorcycle is implemented with road simulator produced by MTS company. Firstly, by disposing strain gauge and accelerometer, the load spectrum of motorcycle is sampled when the motorcycle runs on gravel road and concrete road with constant speed. Then using the front and rear axle acceleration as desired response signals, the simulation iteration and road simulation test are conducted. At last, the key problems of motorcycle road simulation test are solved. The result shows that the desired response signals and the monitor signals are well matched, and the road load spectrum of motorcyle is accurately reproduced.

Adaptive null phase-shift polarization filter

The estimation of clutter polarization is significant in virtual polarization filtering. This paper proposes variable step size Least Mean Square error (LMS) algorithm to estimate clutter polarization. The adaptive recursive function is set up based on double polarization receiving signal model. By choosing desired response signal properly, the received data are estimated. The result of estimation is used for Null Phase-Shift instantaneous Polarization Filter (NPSPF) design and target’s amplitude and phase compensation. Algorithm’s tracking performance is discussed in detail. The simulation results are consistent with the above analysis, and the clutter suppression is effective.

Stochastic mean-square performance analysis of an adaptive Hammerstein filter

This paper presents an almost sure mean-square performance analysis of an adaptive Hammerstein filter for the case when the measurement noise in the desired response signal is a martingale difference sequence. The system model consists of a series connection of a memoryless nonlinearity followed by a recursive linear filter. A bound for the long-term time average of the squared a posteriori estimation error of the adaptive filter is derived using a basic set of assumptions on the operating environment. This bound consists of two terms, one of which is proportional to a parameter that depends on the step size sequences of the algorithm and the other that is inversely proportional to the maximum value of the increment process associated with the coefficients of the underlying system. One consequence of this result is that the long-term time average of the squared a posteriori estimation error can be made arbitrarily close to its minimum possible value when the underlying system is time-invariant.

Fast QRD-lattice-based unconstrained optimal filtering for acoustic noise reduction

We derive a fast QRD-least-squares lattice (QRD-LSL) based unconstrained optimal filtering algorithm for multichannel acoustic noise reduction. As known from the literature, the unconstrained optimal filtering approach is an alternative to the popular GSC beamforming, which does not rely on a priori information and hence possesses improved robustness. The optimal filtering problem involved is special in that the desired response signal is not known explicitly. The derivation of the QRD-LSL algorithm is based on a significantly reorganized version of a QRD-RLS-based unconstrained optimal filtering scheme. Overall an order of magnitude complexity reduction is obtained without any performance penalty, which makes this new approach affordable for real time implementation.

An approach to reducing the effect of contamination on the desired response of a frequency domain adaptive filter

In this paper, the frequency domain signal restoration problem is considered, where the desired response signal is contaminated by noise. The circumstances in which this problem arises are outlined and the impact on filter performance is described. We use a Bayesian framework to derive a solution that can be viewed as an approximate MAP estimator for the optimal filter response. The performance of this estimator is analyzed through both theory and simulation.

GSVD-based optimal filtering for single and multimicrophone speech enhancement

A generalized singular value decomposition (GSVD) based algorithm is proposed for enhancing multimicrophone speech signals degraded by additive colored noise. This GSVD-based multimicrophone algorithm can be considered to be an extension of the single-microphone signal subspace algorithms for enhancing noisy speech signals and amounts to a specific optimal filtering problem when the desired response signal cannot be observed. The optimal filter can be written as a function of the generalized singular vectors and singular values of a speech and noise data matrix. A number of symmetry properties are derived for the single-microphone and multimicrophone optimal filter, which are valid for the white noise case as well as for the colored noise case. In addition, the averaging step of some single-microphone signal subspace algorithms is examined, leading to the conclusion that this averaging operation is unnecessary and even suboptimal. For simple situations, where we consider localized sources and no multipath propagation, the GSVD-based optimal filtering technique exhibits the spatial directivity pattern of a beamformer. When comparing the noise reduction performance for realistic situations, simulations show that the GSVD-based optimal filtering technique has a better performance than standard fixed and adaptive beamforming techniques for all reverberation times and that it is more robust to deviations from the nominal situation, as, e.g., encountered in uncalibrated microphone arrays.

Analysis of an anti-Hebbian adaptive FIR filtering algorithm

In this brief, a recently proposed adaptive FIR filtering algorithm based upon an anti-Hebbian learning scheme is analyzed. Approximate evolution equations for the mean coefficient vector and coefficient correlation matrix are derived assuming noise-corrupted input and desired response signals. From our analysis, it is shown that the proposed algorithm can achieve an unbiased estimate of the unknown system in a system identification task if the input noise is uncorrelated with the same variance as the output noise. In more general situations, the solution obtained by the algorithm is biased. Moreover, the adaptive filter coefficients can diverge if the signal-to-noise ratio of the desired response signal is sufficiently small. Simulations verify the theoretical predictions and indicate the range of behaviors of the anti-Hebbian learning scheme for FIR system identification tasks.

Adaptive lattice bilinear filters

Two fast least-squares lattice algorithms for adaptive nonlinear filters equipped with bilinear system models are presented. The lattice filter formulation transforms the nonlinear filtering problem into an equivalent multichannel linear filtering problem, thus using multichannel lattice filtering algorithms to solve the nonlinear filtering problem. The computational complexity of the algorithms is an order of magnitude smaller than that of previously available methods. The first of the two approaches is an equation error algorithm that uses the measured desired response signal directly to compute the adaptive filter outputs. This method is conceptually very simple, but results in biased system models in the presence of measurement noise. The second is an approximate least-squares output error solution; the past samples of the output of the adaptive system itself are used to produce the filter output at the current time. Results indicate that the output error algorithm is less sensitive to output measurement noise than the equation error method. >

Steady-state analysis of a single-layer perceptron based on a system identification model with bias terms

A stochastic analysis is presented of the steady-state convergence properties of a single-layer perceptron for Gaussian input signals. A system identification formulation is presented whereby the desired response signal (+or-1) is modeled by an unknown linear FIR system F plus an unknown bias, followed by a signum function nonlinearity. The perceptron nonlinearity is based on the error function, which implements the signum function as a special case, and it also includes a bias adjustment. It is demonstrated that the converged adaptive weights of the perceptron are proportional to F, and the proportionality constant is infinite when the bias terms are set to zero. If the bias terms are both nonzero, the converged perceptron weights have a unique finite solution determined by the bias factor magnitudes. >

Efficient block LS design of FIR filters with linear phase

This paper is concerned with the efficient determination of FIR least squares filters with linear phase. The general unknown statistics case is considered, whereby only sample records of the input and the desired response signals are available. Both linear phase cases with phase delay and group delay are examined. Taking advantage of the underlying near-to-Toeplitz structure of the resulting normal equations, a fast algorithm is developed that recursively produces all linear phase filters orders for a single block of data.

Efficient recursive in order least squares FIR filtering and prediction

This paper is concerned with the efficient determination of the optimum, in the least squares sense, FIR filter on the basis of data samples of the input and desired response signals, by procedures recursive in the filter order. This situation typically arises when no a priori statistics are available and the system order is not known. The general multiinput-multioutput (multichannel) case is considered here and a fast algorithm is presented requiring for single channel signals approximately 2S + 15m multiplications (mps) per order m, S being the number of samples. In the special case of linear prediction it calls for about S + 12m mps. Hence it offers a computational reduction of 5m and 2m mps in comparison to the methods of Marple [1] and Morf et al. [2], respectively. Additionally, the proposed scheme is inherently symmetric and is suited very well to initialization of fast sequential algorithms as well as algorithms searching for the optimum lag filter.