混响和有色噪声环境下的顽健时延估计方法

提出一种针对混响和有色噪声的顽健时延估计算法.该算法通过对房间冲激响应进行盲辨识来去除混响的影响,并使用延迟相关矩阵抑制有色噪声,从而提高了实际环境下时延估计算法的性能.仿真实验结果表明,在混响和有色噪声环境下,此算法能有效地进行时延估计.     

Robust estimation and signal detection with dependent nonstationary data

In this paper the dual topics of robust signal detection and robust estimation of a random variable are considered, where the data may be both dependent and nonstationary. We note that classical saddlepoint techniques for robustness do not readily apply in the dependent and/or nonstationary situation, and thus our results have application in a larger domain than what was feasible heretofore. In addition, our methods make possible the quantitative measurement of robustness and admit essentially arbitrary perturbations in an underlying joint statistical distribution away from the nominal. In particular, our methods show that the presence of dependency can result in a reduction of the robustness of the linear detector by approximately 50% and that appropriate censoring can improve this situation. We also show that, somewhat surprisingly, a weak amount of censoring can actually reduce robustness rather than increase it, even with dependent data that is almost independent. This calls into question the common practice, inspired by classical saddlepoint results for independent data, of employing censoring in cases where residual dependency is conceded. When applied to estimation, our work shows that for nominally Gaussian data, the conditional expectation estimator is optimal not only in terms of performance but also robustness (under appropriate performance measures), thus reinforcing the appeal of this estimator. On the other hand, for other performance measures, we also note that the conditional expectation estimator can be completely unrobust, regardless of whether the data is nominally Gaussian or not. Finally, our results establish a bound on estimator robustness.This research was supported by the Air Force Office of Scientific Research under Grant AFOSR-91-0267.     

Wavelet-based estimation of the nonstationary mean signal inwireless systems

A new technique is described for estimating the nonstationary mean signal received at a mobile station in a Rayleigh fading environment. The estimate is based on samples taken at the midpoints between the local minima of the received envelope. The continuous wavelet transform is used to estimate the local minima. An estimate of the mean signal is obtained using a fixed number of local minima. This technique requires neither an estimate of the mobile speed nor an adaptive temporal averaging window in contrast to other estimators. Simulations show that the mean signal is estimated well in a nonstationary environment with variable mobile speed     

Adaptive estimation of latency change in evoked potentials bydirect least mean p-norm time-delay estimation

Evoked potentials (EP) have been widely used to quantify neurological system properties. Changes in EP latency may indicate impending neurological injury. Traditional EP analyses are developed under the condition that the background noise in EP analysis are Gaussian distributed. This paper proposes a latency change detection and estimation algorithm under α-stable noise condition, a generalization of Gaussian noise assumption. An analysis shows that the α-stable model fits the noises found in the impact acceleration experiment under study better than the Gaussian model. The robustness of the proposed algorithm is demonstrated through computer simulations and experimental data analysis under both Gaussian and α-stable noise environments     

Multichannel post-filtering in nonstationary noise environments

In this paper, we present a multichannel post-filtering approach for minimizing the log-spectral amplitude distortion in nonstationary noise environments. The beamformer is realistically assumed to have a steering error, a blocking matrix that is unable to block all of the desired signal components, and a noise canceller that is adapted to the pseudo-stationary noise but not modified during transient interferences. A mild assumption is made that a desired signal component is stronger at the beamformer output than at any reference noise signal, and a noise component is strongest at one of the reference signals. The ratio between the transient power at the beamformer output and the transient power at the reference noise signals is used to indicate whether such a transient is desired or interfering. Based on a Gaussian statistical model and combined with an appropriate spectral enhancement technique, we derive estimators for the signal presence probability, the noise power spectral density, and the clean signal. The proposed method is tested in various nonstationary noise environments. Compared with single-channel post-filtering, a significantly reduced level of nonstationary noise is achieved without further distorting the desired signal components.     

Adaptive modeling and spectral estimation of nonstationary biomedical signals based on Kalman filtering

We describe an algorithm to estimate the instantaneous power spectral density (PSD) of nonstationary signals. The algorithm is based on a dual Kalman filter that adaptively generates an estimate of the autoregressive model parameters at each time instant. The algorithm exhibits superior PSD tracking performance in nonstationary signals than classical nonparametric methodologies, and does not assume local stationarity of the data. Furthermore, it provides better time-frequency resolution, and is robust to model mismatches. We demonstrate its usefulness by a sample application involving PSD estimation of intracranial pressure signals (ICP) from patients with traumatic brain injury (TBI).     

Digital algorithm for maximisation of symmetric ambiguity functionsand application to signal time-delay estimation

Ambiguity functions are usually symmetric around their maximum. In such a case, a consistent estimator of their median value can also detect their maximum. A digital algorithm searches for the zero-crossing point of the Hilbert transform of the estimated function samples. The performance of such a method is analysed by a reduced Taylor expansion, depending on the second-order statistics of the estimated ambiguity samples. The accuracy is explicitly provided in the case of time-delay estimation between random Gaussian signals, corrupted by Gaussian noises. The optimal length of an FIR implementation of the Hilbert filter is also discussed with reference to the generalised cross-correlation method     

Adaptive maximum-likelihood sequence estimation by means ofcombined equalization and decoding in fading environments

This paper proposes an adaptive maximum-likelihood sequence estimation (MLSE) by means of combined equalization and decoding, i.e., adaptive combined MLSE, which employs separate channel estimation for respective states in the Viterbi algorithm. First, an approximate metric including channel estimation is derived analytically for this proposed adaptive combined MLSE. Secondly, procedures to accomplish blind equalization are investigated for the proposed MLSE. Finally, its excellent BER performance on fast time-varying fading channels is confirmed by computer simulation, when the proposed MLSE operates as a blind equalizer     

Adaptive estimation of residue signal for voice pathology diagnosis

The use of noninvasive techniques to evaluate the larynx and vocal tract helps the speech specialists to perform accurate diagnose of diseases. In this study, a method to distinguish among 21 different pathologies using speech signals was developed. Through inverse filtering (Kalman and Wiener filters) of the voice signal, the residue was estimated and seven acoustic features were extracted from it to evaluate the laryngeal diseases. As time-invariant inverse filtering was used, the nonstationary nature of dysphonic voices was also considered. Together with the estimation of the acoustic features using a robust statistical method, this technique also allowed us to discriminate among pathologies with very close perceptual characteristics. The results from a Mann-Whitney test indicated that the best measurement for pathological discrimination was JITTER with 54.79% ability to cluster the voice types and the worst one was spectral flatness of residue (SFR) with 36.41%     

A new time-delay estimation in multipath

This paper addresses a new approach to time-delay estimation based upon the autocorrelation estimator (AE). The primary aim of this paper is to estimate time-delays in a multipath environment in absence of prior knowledge of the channel. The maximum likelihood estimator (MLE) and AE are two computational tools that are used to determine the parameters of a multipath channel. MLE requires some priori knowledge of the source signal and the channel; AE can be a blind estimator but it is more suitable for a simple propagation model (one extra path). Under the multipath assumption we prove that if the observation sequence is zero padded the performance of MLE exceeds that of AE, however, at the price of higher computational efforts. The general autocorrelator estimator (GAE), based on autocorrelation of the received signal, is introduced. The GAE is formulated as a blind estimator, and the pertinent Cramer-Rao lower bounds (CRLB) are derived. We also develop an algorithm to estimate the parameters of a multipath environment based on the new generalization. The performance of this algorithm is examined for different signal-noise scenarios. Our results show that the time-delays are estimated accurately based on the proposed algorithm.     

Adaptive filtering of the electromyographic signal for prostheticcontrol and force estimation

An adaptive time constant filter is derived for electromyographic (EMG) signal processing in prosthetic control applications. The analysis indicates that the mean-squared estimation error can be reduced by varying the time constant of the filter as a function of the signal and its derivative. Results of several experiments indicated this filter provides faster response and smaller estimation error than several previously available filters     

A parallel adaptation algorithm for recursive-least-squaresadaptive filters in nonstationary environments

An accurate new expression for the steady-state tracking performance of exponentially weighted recursive-least-squares (RLS) adaptive filters in a random walk scenario is derived. This relation is then used to provide a detailed comparison between RLS-performance and that of normalized least-mean-squares adaptive filters. Further, a variable-forgetting-factor algorithm referred to as the parallel adaptation algorithm that approximately achieves the theoretical minimum mean-squared-error performance in a random walk scenario is developed. Extensive simulation results are presented to support the present findings and demonstrate the improved performance of the proposed algorithm in a number of different applications     

Joint time-delay estimation and adaptive recursive least squaresfiltering

A general estimation model is defined in which two observations are available: a noisy and a noise-filtered and delayed version of the transmitted signal. The delay and the filter must be estimated. The joint estimator is composed of an adaptive delay element operating in conjunction with an adaptive transversal filter. The delay is updated using a form of derivative, with respect to the delay, of the sum of squared errors. The adaptive delay is limited to integer values and is defined as the lag. The lag value is computed and updated so that the optimum least-squares solution is attained. The joint algorithm is obtained by combining the lag update relations with a version of the fast transversal filter RLS algorithm. Simulations show that both stationary and time-varying delays are effectively tracked and that the adaptive filter properly estimates the reference filter impulse response     

Subspace-based joint time-delay and frequency-shift estimation inasynchronous DS-CDMA systems

A subspace-based algorithm for joint time-delay and frequency shift estimation in asynchronous DS-CDMA systems is proposed. The algorithm is near-far robust, requires no preamble, is well suited to fading channels, and can be used for both single-user or multiuser estimation     

AM/FM signal estimation with micro-segmentation and polynomial fit

Amplitude and phase estimation of AM/FM signals with parametric polynomial representation require the polynomial orders for phase and amplitude to be known. But in reality, they are not known and have to be estimated. A well-known method for estimation is the higher-order ambiguity function (HAF) or its variants. But the HAF method has several reported drawbacks such as error propagation and slowly varying or even constant amplitude assumption. Especially for the long duration time-varying signals like AM/FM signals, which require high orders for the phase and amplitude, computational load is very heavy due to nonlinear optimization involving many variables. This paper utilizes a micro-segmentation approach where the length of segment is selected such that the amplitude and instantaneous frequency (IF) is constant over the segment. With this selection first, the amplitude and phase estimates for each micro-segment are obtained optimally in the LS sense, and then, these estimates are concatenated to obtain the overall amplitude and phase estimates. The initial estimates are not optimal but sufficiently close to the optimal solution for subsequent processing. Therefore, by using the initial estimates, the overall polynomial orders for the amplitude and phase are estimated. Using estimated orders, the initial amplitude and phase functions are fitted to the polynomials to obtain the final signal. The method does not use any multivariable nonlinear optimization and is efficient in the sense that the MSE performance is close enough to the Cramer–Rao bound. Simulation examples are presented.     

Rician envelope estimation and confidence intervals in low signal/noise levels

The determination of a Rician signal envelope in low signal/noise is a biased estimate of the true (noiseless) envelope. Four statistical estimators which attempt to correct for the biasing are investigated, namely the maximum likelihood, most probable, mean and median estimators. Confidence intervals on the envelope estimation are also presented at the 68%, 95% and 99% confidence levels.     

Signal-selective time-difference-of-arrival estimation for passivelocation of man-made signal sources in highly corruptive environments.I. Theory and method

For the problem of estimating time difference of arrival (TDOA) of radio waves impinging on a pair of antennas for the purpose of passively locating the source of a communications or telemetry signal in the presence of interfering signals and noise, a new class of signal-selective algorithms that are highly tolerant to interference and noise is introduced. Unlike conventional methods, the new methods cross correlate frequency-shifted as well as time-shifted versions of the received data in order to exploit the unique cyclostationarity property of the signal of interest     

Second-order-based blind signal separation in reverberant environments

The aim of this paper was to provide a derivation to explain the performance of the second-order-based blind signal separation (BSS) in reverberant environments. In particular, the second-order-based BSS algorithm, which exploits non-stationarity of the input signals, is investigated. The derivation provides a quantitative link between the reverberation parameter of the environment with the cost function of the BSS. Importantly, the theoretical finding complements existing literature on the interpretation of BSS and provides incremental insight to its performance.