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A novel robust Kalman filter (KF)-based controller design approach is proposed to accurately track a specified trajectory under unknown stochastic disturbance, known deterministic disturbance, and measurement noise. The system is nonlinear and is approximated by a piecewise-linear dynamic model. The Box–Jenkins model is an augmented model of the signal and the disturbance, is non-controllable and observable, while the signal model is controllable and observable. An emulator-based two-stage identification is employed to obtain an accurate system model needed to design the robust controller. The system and its KF are identified, and the signal and output errors are estimated. From the identified models, the signal, its KF, the disturbance model, and the whitening filter are all obtained using balanced model reduction techniques. It is shown that the signal model is a transfer matrix description relating the system output and KF residual, and the residual is the whitened output error. The disturbance model is identified by inverse filtering. A new combined feedforward–feedback controller is designed and implemented using an internal model of the reference driven by both the error between the reference and the signal estimate, and by the feedforwarded reference signal. The proposed scheme was successfully evaluated on a simulated autonomously guided drone. [ABSTRACT FROM AUTHOR]

The extraction and identification of microleakage signal caused by the corrosion of gathering pipelines in the oil and gas fields is a great technical challenge for leakage monitoring. Therefore, a microleakage locating algorithm of gas gathering pipeline based on whitening filter was proposed for the microleakage signal submerged in the pipeline.noise As the acquired signal of gathering pipeline contains an abundance of low-frequency components, tracking with the adaptive whitening filter can eliminate the low-frequency components, so that the power spectrum of the acquired signal becomes flat and close to the white noise. The microleakage signal processed by whitening filter is enhanced, and the signal-to-noise ratio (SNR) is increased by nearly 4 dB. At the same time, the cross-correlation operation with the signals processed by whitening filter can calculate the time delay of signals at both ends of pipline, futher locate the microleakage point of gathering pipeline by leakage positioning formula. According to the calculation results, the algorithm can accurately locate the microleakage signal of the oil and gas field gathering pipeline, with error of 2 m. Thus, demonstration application should be provided for various media at different pressure levels under different topographic conditions, so as to improve the locating accuracy of the algorithm, further providing technical support for the construction of no-leakage demonstration area.

The long-time coherent integration can effectively improve the detection ability of radar targets. However, this strategy usually shows poor effect in resisting the sea clutter, which produces difficulties for accurate estimation of sea clutter characteristics and results in the inability to differentiate between the target and sea clutter. To solve this problem, a two-stage method is proposed, which consists of the sea clutter suppression stage and target decision stage. In the sea clutter suppression stage, the correlation time differences in the time and the space domains are adopted to estimate the correlation of sea clutter. Then, a selective whitening filter is proposed, which is performed more adaptively according to the estimation results. In the decision stage, the peak average ratio in the fractional Fourier domain (FRFT-PAR) is presented, which can make better use of the energy accumulation characteristics and further suppress the interference of sea clutter. Experiments on the IPIX datasets with various observation times and polarization modes are included. The results indicate that the proposed method could not only effectively suppress sea clutter but also achieve better target detection performance than baseline algorithms. [ABSTRACT FROM AUTHOR]

The accurate estimation of clutter covariance matrix (CCM) is essential in designing a radar detector/filter to suppress sea clutter. This estimation might not be easily accomplished because of the scarcity of valid training vectors adjacent to the range cell under test (CUT). We propose a new CCM estimation algorithm that is derived by modeling time-series clutter returns into a clutter Doppler spectrum in the frequency domain and exploiting mutual independence among spectral components. To justify its excellence over the conventional sample covariance matrix (SCM) algorithm, we design two filters—a maximum signal-to-interference-plus-noise ratio (SINR)-based filter and a whitening filter—that use the estimated CCMs and compare their performance in a numerically simulated sea clutter scenario. Comparisons are made by showing the eigenvector spectra of the estimated CCMs and the frequency responses and outputs of the filters. Moreover, SINRs at the target Doppler bin are examined and compared with a theoretical, analytically derived SINR.

In this paper the Cramer-Rao information bound for ARMAX (Auto-Regression-Moving-Average-Models-with-Exogenuos-inputs) under non-Gaussian noise is derived. It is shown that the direct application of the Least Squares Method (LSM) leads to incorrect (shifted) parameter estimates. This inconsistency can be corrected by the implementation of the parallel usage of the MLMW (Maximum Likelihood Method with Whitening) procedure, applied to all measurable variables of the model, and a nonlinear residual transformation using the information on the distribution density of a non-Gaussian noise, participating in Moving Average structure. The design of the corresponding parameter-estimator, realizing the suggested MLMW-procedure is discussed in details. It is shown that this method is asymptotically optimal, that is, reaches this information bound. If the noise distribution belongs to some given class, then the Huber approach (min-max version of MLM) may be effectively applied. A numerical example illustrates the suggested approach. [ABSTRACT FROM AUTHOR]

The long-time coherent integration can effectively improve the detection ability of radar targets. However, this strategy usually shows poor effect in resisting the sea clutter, which produces difficulties for accurate estimation of sea clutter characteristics and results in the inability to differentiate between the target and sea clutter. To solve this problem, a two-stage method is proposed, which consists of the sea clutter suppression stage and target decision stage. In the sea clutter suppression stage, the correlation time differences in the time and the space domains are adopted to estimate the correlation of sea clutter. Then, a selective whitening filter is proposed, which is performed more adaptively according to the estimation results. In the decision stage, the peak average ratio in the fractional Fourier domain (FRFT-PAR) is presented, which can make better use of the energy accumulation characteristics and further suppress the interference of sea clutter. Experiments on the IPIX datasets with various observation times and polarization modes are included. The results indicate that the proposed method could not only effectively suppress sea clutter but also achieve better target detection performance than baseline algorithms.

In this paper the Cramer-Rao information bound for ARMAX (Auto-Regression-Moving-Average-Models-with-Exogenuos-inputs) under non-Gaussian noise is derived. It is shown that the direct application of the Least Squares Method (LSM) leads to incorrect (shifted) parameter estimates. This inconsistency can be corrected by the implementation of the parallel usage of the MLMW (Maximum Likelihood Method with Whitening) procedure, applied to all measurable variables of the model, and a nonlinear residual transformation using the information on the distribution density of a non-Gaussian noise, participating in Moving Average structure. The design of the corresponding parameter-estimator, realizing the suggested MLMW-procedure is discussed in details. It is shown that this method is asymptotically optimal, that is, reaches this information bound. If the noise distribution belongs to some given class, then the Huber approach (min-max version of MLM) may be effectively applied. A numerical example illustrates the suggested approach.

This article designs waveform in fast-time domain to optimise high-frequency radar performances embedded in the co-channel interference and coloured noise. Considering the whitening filter is applied for fast-time processing, a novel objective function accounting for both signal to interference plus noise ratio (SINR) and integrated range sidelobe level (IRSL) is introduced and the constant modulus constraint is forced on the probing waveform. An iterative algorithm based on the alternating direction method of multipliers is presented, resolving such non-convex quartic optimisation problem with a polynomial-time computational complexity. In each iteration, two quadratic problems are presented, with the closed-form solutions given. Finally, the authors evaluate the effectiveness of the designed waveform in terms of the range-Doppler processing result, SINR, IRSL and power spectrum.

This article designs waveform in fast-time domain to optimise high-frequency radar performances embedded in the co-channel interference and coloured noise. Considering the whitening filter is applied for fast-time processing, a novel objective function accounting for both signal to interference plus noise ratio (SINR) and integrated range sidelobe level (IRSL) is introduced and the constant modulus constraint is forced on the probing waveform. An iterative algorithm based on the alternating direction method of multipliers is presented, resolving such non-convex quartic optimisation problem with a polynomial-time computational complexity. In each iteration, two quadratic problems are presented, with the closed-form solutions given. Finally, the authors evaluate the effectiveness of the designed waveform in terms of the range-Doppler processing result, SINR, IRSL and power spectrum. [ABSTRACT FROM AUTHOR]

The authors propose a robust artificial bandwidth extension (ABE) technique to improve narrowband (NB) speech signal quality using an enhanced spectrum envelope and excitation estimation. For envelope estimation, they propose an enhanced envelope estimation method using a deep neural network with multiple layers. For excitation estimation, they use a whitened NB excitation signal that is generated by passing the excitation signal through a whitening filter. An adaptive spectral double shifting method is introduced to obtain an enhanced wideband (WB) excitation signal. The proposed ABE system is applied to the decoded output of an adaptive multi‐rate (AMR) codec at 12.2 kbps. They evaluate its performance using log spectral distortion, a WB perceptual evaluation of speech quality, and a formal listening test. The objective and subjective evaluations confirm that the proposed ABE system provides better speech quality than AMR at the same bit rate. [ABSTRACT FROM AUTHOR]

A solution to the problem of reliable discrimination between the move and stop states of a mine lift is proposed based on processing signals of an accelerometer mounted in the lift cabin. The accelerometer signal is processed using a combination of adaptive whitening algorithms and a nonparametric Neyman-Pearson test that provides an effective solution of the problem under a priori nonparametric uncertainty conditions. [ABSTRACT FROM AUTHOR]

This work proposes a QRS complex detector which works on high amplitude. An optimized nonlinear adaptive whitening filter based on sigmoidal radial basis function (SRBF) is used to cleverly accentuate the QRS complexes and suppress the noises present in the ECG signal. An M-estimator based objective function and a new contextually based centering technique are used to control the behavior of the whitening filter so that it totally suppresses low frequencies segments and preserves almost unchanged the high frequencies ones. The residual issued from the whitening filter is then fed to a matched filter to enhance the signal-to-noise ratio (SNR) which is yet an important step that allows achieving good detection performance. Additional nonlinear transformations are performed before the decision stage namely squaring and moving average filtering. The validation of the detector is performed over the whole MIT/BIH database files. The algorithm has produced an average detection error rate of 0.28%, sensitivity of 99.82% and specificity of 99.91%. [ABSTRACT FROM AUTHOR]

SUMMARY This paper proposes a new method of estimating accurately the modal damping of a weakly damped electromechanical oscillation in power systems. The proposed method is based on a whitening filter (finite impulse response filter) to estimate the modal damping from the system response and a regression model to express the relation between the modal damping and system state variables, such as the active and reactive power outputs of generators. The feature of the method is that an optimal order of the whitening filter is determined so as to minimize the error of the linear regression model. A simulation study using the IEEJ EAST 10 machine model shows that the proposed method is effective for accurate estimation of weakly damped oscillation modes. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we study a molecular communication system operating over a moving propagation medium. Using the convection–diffusion equation, we present the first separate models for the channel response and the corrupting noise. The flow-based molecular channel is shown to be linear but time-varying and the noise corrupting the signal is additive white Gaussian with a signal dependent magnitude. By modelling the ligand–receptor binding process, it is shown that the molecular communication reception process in this channel has a low-pass characteristic that colours the additive noise. A whitening filter is proposed to compensate for this low-pass characteristic. Simulation results demonstrate the benefit of the whitening filter and the effect of medium motion on bit error rate. [Copyright &y& Elsevier]

Independent component analysis (ICA) has been widely applied to the analysis of fMRI data. Accurate estimation of the number of independent components (ICs) in fMRI data is critical to reduce over/underfitting. Various methods based on information theoretic criteria (ITC) have been used to estimate the intrinsic dimension of fMRI data. An important assumption of ITC is that the noise is purely white. However, this assumption is often violated by the existence of temporally correlated noise in fMRI data. In this study, we introduced a filtering method into the order selection to remove the autocorrelation from the colored noise by using the whitening filter proposed by Prudon and Weisskoff. Results of the simulated data show that the filtering method has strong robustness to noise and significantly improves the accuracy of order selection from data with colored noise. Moreover, the multifiltering method proposed by us was applied to real fMRI data to improve the performance of ITC. Results of the real fMRI data show that the proposed method can alleviate the overestimation due to the autocorrelation of colored noise. We further compared the stability of IC estimates of real fMRI data at order estimated by minimum description length criterion based on the filtered and unfiltered data by using the software package ICASSO. Results show that ICA yields more stable IC estimates using the reduced order by filtering. [ABSTRACT FROM PUBLISHER]

Cavitation has become the main cause of damage to hydraulic machinery. Monitoring and detection of cavitation is necessary to avoid severe consequences. The sound, especially the audible sound-based method, is becoming attractive due to its simplicity and logicality. However, cavitation noise is easily contaminated by surrounding environmental noise, ie background noise, which is always coloured and cannot be eliminated easily by a wavelet transform (WT)-based method, although this has its merits and is very popular and effective. In this paper, in order to de-noise cavitation noise and eliminate coloured background noise efficiently, a de-noising strategy is proposed, which combines the whitening filter technique and the WT-based de-noising method. The main idea of the proposed de-noising strategy is that the whitening filter is constructed first with respect to the background noise, and then the constructed whitening filter is used to pre-process the cavitation noise so that the included background noise can be whitened before the WT-based method is used finally for the de-noising process. A simulation is designed to validate the effect of the proposed de-noising strategy. The experimental study is also presented to demonstrate its effect in de-noising cavitation noise for cavitation detection and state monitoring. [ABSTRACT FROM AUTHOR]

In this paper the problem of a physically realizable whitening-filter (WF) synthesis for cryogenic resonant gravitational-wave antennas is considered in the framework of the optimal linear Kalman-Bucy filtering. A system of equations determining the structure of the Kalman filter for gravitational-wave antennas with a displacement converter is presented. The transfer function of a stationary physically realizable WF is derived. The Kalman approach ensures the possibility of high frequency parameter measurements of Gaussian and non-Gausssian noises in low-dissipation oscillatory systems for observation intervals, which are relatively short in comparison to the relaxation time. [ABSTRACT FROM AUTHOR]

The problem of automatic segmentation of a stochastic time series into homogeneous data segments one cluster long is posed and solved according to the general formulation of the disorder problem. A new algorithm with cluster normalization by the variance of the generating noise is developed using an autoregression model and the minimum information divergence criterion. It is shown that the main advantage of the proposed algorithm over existing analogs is high dynamic properties. The algorithm was tested by analyzing stock market dynamics in the United States and Russia. Estimates of the admissible (threshold) level of disorder in a time series within one cluster are obtained using the Kullback-Leibler information measure. [ABSTRACT FROM AUTHOR]

Block diagonalization is one approach for linear preceding in the multiple-input multiple-output broadcast channel that sends multiple interference free data streams to different users in the same cell. Unfortunately, block diagonalization neglects other-cell interference (OCI), which limits the performance of users at the edge of the cell. This paper presents an OCI-aware enhancement to block diagonalization that uses a whitening filter for interference suppression at the receiver and a novel precoder using the interference-plus-noise covariance matrix for each user at the transmitter. For complex Gaussian matrix channels, the asymptotic sum rate of the proposed system is analyzed under a large antenna assumption for isotropic inputs and compared to conventional block diagonalization. The capacity loss due to OCI is quantified in terms of results from single-user MIMO capacity. Several numerical examples compare achievable sum rates, the proposed asymptotic rates, and the capacity loss, in low and high interference regimes. [ABSTRACT FROM PUBLISHER]

Abstract: Extensive work to develop and optimise signal processing for signals that are corrupted by additive Gaussian noise has been done so far mainly because of the central limit theorem and the ease in analytic manipulations. It has been observed that the algorithms designed for Gaussian noise typically perform poor in presence of Gaussian mixture (non-Gaussian) noise. This paper discusses a likelihood based algorithm using kernel density estimates to improve channel estimation over a block in non-Gaussian noise environments. The likelihood pdf is assumed unknown and is estimated by using kernel density estimator at the receiver. A novel technique for channel estimation using a whitening filter for interference limited channels is also proposed in this paper. The performance of the proposed estimator is compared with the Cramér Rao lower bound for associated noise distribution. The simulations for impulsive noise and co-channel interference in presence of Gaussian noise, confirms that a better estimate can be obtained by using the proposed technique as compared to the traditional least-squares-based algorithms in highly non-Gaussian environments. [Copyright &y& Elsevier]

Abstract: Magnetic anomaly detection (MAD) is a passive method for detection of a ferromagnetic target. A magnetic field generated by a ferromagnetic target is assumed as a dipole field, resulting in an anomaly in the ambient Earth magnetic field. Magnetic anomaly detection method using orthonormal basis functions (OBF) decomposition is a known approach. The method, which relies on matched filtering, should be optimal for detection of a known signal in the presence of Gaussian white noise. In this work, we expand the method to the more general case of magnetic noise with a power spectral density of 1/f α , where 0< α <2. We have designed a whitening filter, which improves magnetic anomaly detection using the basis functions decomposition approach. It turned out that the measured magnetic noise, comprising intrinsic sensor noise and geomagnetic noise, may be considered as an autoregressive (AR) process. Thus, by calculating the coefficients of the autoregressive model, we can build a whitening filter. Application of a whitening filter transforms the noise from 1/f α into a white noise, but also distorts the target signal. As a consequence, the original orthonormal basis functions should be modified in order to form a basis for the distorted target signal. The results in the present work demonstrate the advantage of the proposed method. [Copyright &y& Elsevier]