Accurate Signal Estimation Research Articles

A speech separation algorithm based on the comb-filter effect

In the field of speech separation, the traditional single-channel and multi-channel speech separation methods have made great progress. However, the accuracy of separation and automatic speech recognition(ASR) rate are not yet satisfactory. With the development of neural networks, some scholars began to use deep learning to achieve speech separation. Although this kind of method improves the accuracy of speech separation, it also leads to the need for pre-training the model, higher computational complexity and reduced separation performance when the model does not match the mixed signal. This paper has conducted an in-depth study on the scene of multi-speaker separation, and proposed a new dual-channel speech separation algorithm based on the Comb-Filter Effect (CFE). The CFE is an effect that occurs when a signal passes through a first-order differential microphone(FDM) array. And this effect is discovered and exploited for the first time. By using this effect, this paper designed a new signal spectrum estimation method that can realize accurate estimation of speech signal, and combined this method with traditional spectral subtraction to achieve the purpose of speech separation. Finally, this paper compared the proposed algorithm with the traditional FastICA-based algorithm and the fully-convolutional time-domain audio separation network(Conv-TasNet)-based algorithm. The results of simulation and comparison experiments show that the algorithm can effectively separate two-way speech signals while greatly reducing the computational complexity and has excellent robustness. In various situations, the proposed algorithm can obtain the Scale-Invariant Source-to-Noise Ratio improvement (SI-SNRi) of 9.19 dB on average. In addition, the Short-Time Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) of the speech signal can be improved by an average of 33% and 70% or more respectively.

Path loss models for outdoor environment—with a focus on rain attenuation impact on short-range millimeter-wave links

The deployment of a millimeter wave over a short path is one of the keys to enabling technologies for the next generation of wireless communication systems. Path loss (PL) is the most important parameter to indicate the performance of the mm-wave wireless channel. However, the accuracy and efficiency of each model are limited to characterize path loss for an environment that is different in terms of weather conditions and geographical arrangement from that for which they have been designed. This paper analyzed path loss for accurate signal estimation in Malaysia based on outdoor microcellular at 38GHz on a 300 m path length. The impact of rain attenuation on path loss, path loss exponent (PLE), and shadow fading (SF) have been investigated. This paper also presents two-channel models utilized for simulations in terms of the outdoor Large-Scale Path Loss, the statistical spatial channel model NYUSIM (version 2) developed in 2019 by New York University (NYU) and the 3rd Generation Partnership Project (3GPP) TR 38.900 Release 14 channel mode. Even though the CI and 3GPP models are accurate and suitable in the area where the measurement campaign was carried out in the temperate climate and must need modification for different regions, such as tropical climate. The underestimation can be interpreted because of the difference in AF's attenuation factors (pressure, humidity, temperature, rain rate) calculated by the CI model in the NYUSIM simulations and the attenuation factor (AF) obtained from measurement data. The NYUSIM channel model better estimated the measured data of path loss compared with 3GPP. Thus, the CI model is suitable for outdoor environments.

Rotor Noise-Aware Noise Covariance Matrix Estimation for Unmanned Aerial Vehicle Audition

A noise covariance matrix (NCM) estimation method for unmanned aerial vehicle (UAV) audition is proposed with rotor noise reduction as its primary focus. The proposed NCM estimation method could be incorporated into audio processing algorithms using UAV-mounted microphone array systems. The NCM is formed through accurate estimation of the microphone array input signal's amplitude and phase by using a multi-sensory rotor noise power spectral density (PSD) estimator and a filter formed by exploiting the acoustical relationship between the microphone array and the rotor noise sources, respectively. The estimated NCM aims to be readily incorporable into several source enhancement algorithms to reduce the effects of rotor noise and improve the resultant audio quality. Experiment evaluation using real in-flight UAV rotor noise recordings shows that the estimated NCM significantly improves rotor noise reduction (of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 28$</tex-math></inline-formula> dB) and the quality of the target sound.

Performance analysis and experimental verification of SPAD receivers without photon counting for optical wireless communications

Single photon avalanche diode (SPAD) attracts increasing interests to realize photon-sensitive receivers for optical wireless communications (OWC). In SPAD receivers, the bit information is extracted by photon counting process, which will increase the burden of data processing and storage. To remove complex photon counting process, we propose a scheme of letting recording pulses pass through low-pass finite impulse response (FIR) filter to realize a SPAD receiver without photon counting. Based on the SPAD operation principles, the Monte-Carlo model of SPAD-based OWC system is built and verified with experimental results. Moreover, based on the signal-dependent noise model, a simple and accurate signal estimation and decision algorithm is developed to demodulate the low-pass signal. On this basis, the system error performance under different operation conditions is investigated. The results indicate that utilizing the proposed algorithm, the system error performance is improved 1- to 3-orders of magnitude better than conventional algorithm, which depends on additive Gaussian noise model.

A recursive approach for aeroacoustic phased array measurements in wind tunnels.

Noise-interference suppression and data-processing acceleration are crucial to aeroacoustic measurements with phased array in wind tunnels. In this paper, we develop a "multi-window" beamforming algorithm that recursively processes data based on an array-acquisition model. This spatial filtering algorithm is derived from the Kalman filter theory for signal processing. The simulated results show that by using recursive operations, accurate signal estimation is acquired with incoherent and coherent background noise removed in the presence of both channel noise and phase noise. The convergence rate of this recursive algorithm is faster than the existing algorithms. As a result, considerable storage space and computational resources are saved, while testing defects in wind tunnel measurement are revealed and corrected immediately on-site. It has a great potential for real-time localization of sound sources in a noisy environment.

GPS Swept Anti-Jamming Technique Based on Fast Orthogonal Search (FOS)

Recently, there has been growing demand for GPS-based reliable positioning, with the broadening of a range of new applications that mainly rely on GPS. GPS receivers have, recently, been attractive targets for jamming. GPS signals are received below the noise floor. Thus, they are vulnerable to interference and jamming. A jamming signal can potentially decrease the SNR, which results in disruption of GPS-based services. This paper aims to propose a reliable and accurate, swept anti-jamming technique based on high-resolution spectral analysis, utilizing the FOS method to provide an accurate spectral estimation of the GPS swept jamming signal. resulting in suppressing the jamming signal efficiently at the signal processing stages in the GPS receiver. Experiments in this research are conducted using the SpirentTM GSS6700 simulation system to create a fully controlled environment to test and validate the developed method’s performance. The results demonstrated the proposed method’s capabilities to detect, estimate, and adequately suppress the GPS swept jamming signals. After the proposed anti-jamming module was employed, the software receiver was able to provide a continuous positioning solution during the presence of jamming within a 10 m positioning accuracy.

High-Resolution ISAR Imaging Under Low SNR With Sparse Stepped-Frequency Chirp Signals

To obtain high-resolution imaging while reducing the radar operating bandwidth under a low signal-to-noise ratio (SNR), this article proposes a genetic method for accurate residual radial motion estimation and well-focused imaging of the sparse stepped-frequency chirp signal (SSFCS). First, the signal model is constructed by incorporating the residual radial motion parameters into the dictionary. Then, high-quality high-resolution range profiles (HRRPs) are synthesized by Beta process regression (BPR), which has enhanced flexibility in data description and superior performance in parameter estimation. In addition, the genetic method updates the population, i.e., the candidates for the residual radial motion parameters, iteratively according to the image entropy to meet the required precision for well-focused imaging. Experimental results of Monte Carlo simulations and imaging results of simulated and measured data have demonstrated that the proposed method achieves more accurate estimation in residual radial motion parameters and better-focused imaging than the available methods.

Accurate and Fast Amplitude Estimation of Signal Distorted by Noise and Harmonics for Control of VSI

Arriving at an accurate and fast amplitude estimation of the sine-wave distorted by harmonics and noise is important for the control of voltage source inverters in grid-connected photovoltaic system. In this article, the three-point interpolation discrete Fourier transform method based on the maximum sidelobe decay windows is proposed for accurate amplitude estimation. The proposed amplitude estimator is speeded-up by using the guaranteed stable recursive method. The influence of noise on the proposed amplitude estimator is investigated, and the amplitude estimation error caused by the spectral interference is also analyzed. The accuracy and effectiveness of the proposed method are verified through simulation and experiments, where the state-of-art amplitude estimation methods and the boundaries specified in the Standard IEEE C37.118.1-2011 are considered.

Smart-Monitor: Patient Monitoring System for IoT-Based Healthcare System Using Deep Learning

ABSTRACTAutomated physiological signal monitoring to elderly sick patient is not only for fast access of data but also to get reliable service by accurate prediction by healthcare service provider. To address this challenge, this research focuses on novel Internet of Things (IoT) application-based physiological signal monitoring system to advance e-healthcare system. For the realization of the proposed system, Deep Neural Network-based accurate Signal Prediction and estimation algorithm was employed. The proposed system is prototyped as an advanced electronics component by using an intelligent sensor for signal measurement, National Instrument myRIO for smart data acquisition. Smart-Monitor is designed with intelligent sensor as the consumer product. To validate the proposed Smart-Monitor system, four physiological signal prediction accuracies for two users were computed. In prototype experimental set-up, an average accuracy of 97.2% was obtained. This shows that the proposed automated system is reliable and accurate monitoring is possible. From the experimental result, we validate the proposed system can provide reliable assist and accurate signal prediction.

A Sparse Bayesian Learning Approach for Through-Wall Radar Imaging of Stationary Targets

Through-the-wall radar (TWR) imaging is an emerging technology that enables detection and localization of targets behind walls. In practical operations, TWR sensing faces several technical difficulties including strong wall clutter and missing data measurements. This paper proposes a sparse Bayesian learning (SBL) approach for wall-clutter mitigation and scene reconstruction from compressed data measurements. In the proposed approach, SBL is used to model both the intraantenna signal sparsity and interantenna signal correlation for estimating the antenna signals jointly. Here, the Bayesian framework provides a learning paradigm for sharing measurements among spatial positions, leading to accurate and stable antenna signal estimation. Furthermore, the task of wall-clutter mitigation is formulated as a probabilistic inference problem, where the wall-clutter subspace and its dimension are learned automatically using the mechanism of automatic relevant determination. Automatic discrimination between targets and clutter allows an effective target image formation, which is performed using Bayesian approximation. Experimental results with both real and simulated TWR data demonstrate the effectiveness of the SBL approach in indoor target detection and localization.

Sparse representation-based classification: Orthogonal least squares or orthogonal matching pursuit?

Spare representation of signals has received significant attention in recent years. Based on these developments, a sparse representation-based classification (SRC) has been proposed for a variety of classification and related tasks, including face recognition. Recently, a class dependent variant of SRC was proposed to overcome the limitations of SRC for remote sensing image classification. Traditionally, greedy pursuit based method such as orthogonal matching pursuit (OMP) is used for sparse coefficient recovery due to their simplicity as well as low time-complexity. However, orthogonal least square (OLS) has not yet been widely used in classifiers that exploit the sparse representation properties of data. Since OLS produces lower signal reconstruction error than OMP under similar conditions, we hypothesize that more accurate signal estimation will further improve the classification performance of classifiers that exploiting the sparsity of data. In this paper, we present a classification method based on OLS, which implements OLS in a classwise manner to perform the classification. We also develop and present its kernelized variant to handle nonlinearly separable data. Based on two real-world benchmarking hyperspectral datasets, we demonstrate that class dependent OLS based methods outperform several baseline methods including traditional SRC and the support vector machine classifier.

Estimation of signal and noise for a whole-body photon counting research CT system.

Photon-counting CT (PCCT) may yield potential value for many clinical applications due to its relative immunity to electronic noise, increased geometric efficiency relative to current scintillating detectors, and the ability to resolve energy information about the detected photons. However, there are a large number of parameters that require optimization, particularly the energy thresholds configurations. Fast and accurate estimation of signal and noise in PCCT can benefit the optimization of acquisition parameters for specific diagnostic tasks. Based on the acquisition parameters and detector response of our research PCCT system, we derived mathematical models for both signal and noise. The signal model took the tube spectrum, beam filtration, object attenuation, water beam hardening, and detector response into account. The noise model considered the relationship between noise and radiation dose, as well as the propagation of noise as threshold data are subtracted to yield energy bin data. To determine the absolute noise value, a noise look-up table (LUT) was acquired using a limited number of calibration scans. The noise estimation algorithm then used the noise LUT to estimate noise for scans with a variety of combination of energy thresholds, dose levels, and object attenuation. Validation of the estimation algorithms was performed on our whole-body research PCCT system using semi-anthropomorphic water phantoms and solutions of calcium and iodine. The algorithms achieved accurate estimation of signal and noise for a variety of scanning parameter combinations. The proposed method can be used to optimize energy thresholds configuration for many clinical applications of PCCT.

Frequency estimation of the weighted real tones or resolved multiple tones by iterative interpolation DFT algorithm

Accurate frequency estimation of sinusoidal signal is commonly required in a large number of engineering practice scenarios. In this paper, an iterative frequency estimation algorithm is proposed based on the interpolation of Fourier coefficients of weighted samples. This algorithm is nearly applicable for all conventional window functions. Systematic errors for various windows are presented and the performance of the proposed algorithm is investigated in the presence of white Gaussian noise. The simulation results demonstrate that errors caused by a mistaken location of the spectral line can be significantly reduced. The proposed algorithm is straightforward to implement, has high precision, good compatibility, and is robust against additive noise, all of which make it ideal for accurate frequency estimation in spectral analysis.

Parameter estimation of chirp signal under low SNR

Aiming at solving the problem of great difficulty and low accuracy existing in parameter estimation of chirp signal under low signal-to-noise ratio (SNR) condition, an algorithm of accurate parameter estimation of chirp signal is proposed. First, the algorithm extracts ridge frequency of chirp signal based on Short Time Fourier Transform. Second, the protruding glitch frequencies are eliminated through median filter with proper size and the smoothing frequencies are obtained corresponding to the time. Third, the frequency time frequency-modulated (FM) line is fitted coarsely by the least-square linear fitting method, and some frequency points are removed, which are far away from the FM line. Repeat the process several times until the sample correlation coefficient of the fitted line is in high degree when the optimum chirp line is fitted out, so chirp rate and initial frequency are obtained. Simulation shows that the correct rate of parameter estimation is very high. When SNR is not less than −17 dB, the correct rate is 100%, and when the SNR is −18 dB, the correct rate is still capable of up to 99%. This algorithm has a lower SNR threshold of about −17 dB. When the SNR is greater than threshold, parameter estimation accuracy is close to Cramer Rao low bound (CRLB), higher than parameter estimation based on the phase field method. Measured data experiments show that the algorithm can reasonably fit the chirp line of measured chirp signal, which better characterizes the FM features of chirp signal.

Sliding Mode Observers for Plasma Signal Identification in Remote Laser Welding

In this paper, a model for the emission of a plasma signal during the zinc coated steel metal fusion phenomenon is proposed. Remote laser welding process and system identification of plasma signal based on the sliding mode observer are presented. The dynamic behaviour of the keyhole is captured using experimental data from the photodiode sensor. A multi-input single-output (MISO) model is designed, the equivalent injection signal using a unit vector sliding mode observer is applied to update the dynamic matrix coefficients. The variation in the model parameters due to process disturbances in the laser fusion phenomenon is captured by recursively updating the matrix parameters, using matrix forgetting factor approach. The simulation result shows an accurate estimation of plasma signal emitted during the laser welding process in the presence of process disturbances.

Adaptive Tunable Notch Filter for ECG Signal Enhancement

In this paper, a high performance adaptive tunable notch filter algorithm for accurate estimation of ECG signal is proposed. The power line interference and muscle contraction noise is significantly suppressed using the proposed adaptive notch FIR filter with tunable notch frequency. An important aspect of the proposed filter scheme is that it preserves the selectivity and attenuation at the notch frequency. The filter is optimized and its coefficients are computed such that the noise in ECG signal is minimised in a specified frequency range. The proposed algorithm estimates the frequency of unwanted signal and updates accordingly the filter coefficients for optimum performance. Based on simulation results, it is demonstrated that the proposed technique can be used to accurately extract the ECG information such as heart beat rate from noisy ECG signal and significant improvement in output signal-to-noise ratio (SNR) can be achieved as compared to the normal adaptive notch filter technique.

Compressive manifold learning: Estimating one-dimensional respiratory motion directly from undersampled k-space data

To present and validate a manifold learning (ML)-based method that estimates the respiratory signal directly from undersampled k-space data and that can be applied for respiratory self-gated liver MRI. ML methods embed high-dimensional space data in a low-dimensional space while preserving their characteristic properties. These methods have been used to estimate one-dimensional respiratory motion (low-dimensional manifold) from a set of high-dimensional free-breathing abdominal MR images. These approaches require MR images to be reconstructed first from the acquired undersampled data. Recently, the concept of compressive manifold learning (CML) has been introduced that combines compressed sensing with ML by learning low-dimensional manifolds directly from a partial set of compressed measurements, provided that the sampling satisfies the restricted isometry property. We propose to use the CML concept to extract the respiratory signal directly from undersampled k-space data. Simulation results from free-breathing abdominal MR data show that CML can accurately estimate respiratory motion from highly retrospectively undersampled k-space (up to 25-fold acceleration under ideal assumptions). Prospective free-breathing golden-angle radial two-dimensional (2D) acquisitions further demonstrate the feasibility of the CML method for respiratory self-gating acquisition, estimating the respiratory motion from up to 15-fold accelerated MR data. The proposed method performs accurate respiratory signal estimation from highly undersampled k-space data and can be used for respiratory self-navigated 2D liver MRI.

Computationally Efficient Architecture for Accurate Frequency Estimation with Fourier Interpolation

A simplified DFT-based algorithm and its VLSI implementation for accurate frequency estimation of single-tone complex sinusoid signal are investigated. The proposed algorithm estimates frequency by interpolation using Fourier coefficients. It consists of a coarse search followed by a fine search, and its performance closely achieves the Cramer–Rao low bound (CRLB) even in low SNR region. Moreover, a pipelined triple-mode CORDIC architecture is designed to efficiently support complex multiplication, complex magnitude calculation and real division. The triple-mode CORDIC-based radix-4 architecture is employed for the hardware implementation of the frequency estimator, and is suitable for not only fast Fourier transformation but also accurate frequency estimation. A frequency estimator with 1024-point samples is implemented and verified on FPGA. It works at 215 MHz on a Xilinx XC6VLX240T FPGA device, and uses up 4,161 registers and 6,986 slice LUTs. ASIC synthesis results show that it requires an area of 60K equivalent NAND2 gates with a clock rate of 500 MHz at SMIC 0.18 μm technology. The whole latency of the frequency estimator is 2336 cycles. The proposed architecture provides a good trade off between hardware overhead, estimation performance and computation latency.

Energy-efficient data acquisition for accurate signal estimation in wireless sensor networks

Long‐term monitoring of an environment is a fundamental requirement for most wireless sensor networks. Owing to the fact that the sensor nodes have limited energy budget, prolonging their lifetime is essential in order to permit long‐term monitoring. Furthermore, many applications require sensor nodes to obtain an accurate estimation of a point‐source signal (for example, an animal call or seismic activity). Commonly, multiple sensor nodes simultaneously sample and then cooperate to estimate the event signal. The selection of cooperation nodes is important to reduce the estimation error while conserving the network’s energy. In this paper, we present a novel method for sensor data acquisition and signal estimation, which considers estimation accuracy, energy conservation, and energy balance. The method, using a concept of ‘virtual clusters,’ forms groups of sensor nodes with the same spatial and temporal properties. Two algorithms are used to provide functionality. The ‘distributed formation’ algorithm automatically forms and classifies the virtual clusters. The ‘round robin sample scheme’ schedules the virtual clusters to sample the event signals in turn. The estimation error and the energy consumption of the method, when used with a generalized sensing model, are evaluated through analysis and simulation. The results show that this method can achieve an improved signal estimation while reducing and balancing energy consumption.

Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering

This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain.