Low Signal-to-noise Ratio Signals Research Articles

Two-stage benefits of internal and external noise to enhance early fault detection of machinery by exciting fractional SR

For early mechanical fault diagnosis, stochastic resonance (SR) breaks the previous perception that “noise is useless” and uses internal noise or external noise to enhance weak fault characteristics. Moreover, the fractional-order derivative can reinforce noise-enhanced weak fault detection. However, in the face of extremely low signal-to-noise ratio conditions, the enhancement effect of fractional SR induced by a single excitation of internal noise or external noise is unsatisfactory. To solve the above drawbacks, this paper investigates two-stage benefits of both internal and external noise to enhance early fault detection of machinery by exciting parallel array of fractional SR, and using the signal-to-noise ratio (SNR) to adjust these parameters of fractional SR. Then, two experiments including rolling element bearings and gearboxes were performed to validate it. Experimental results show that the proposed method takes on obvious detection ability for low SNR signals, where the amplitude at the fault characteristic frequency is amplified by beyond 300 times in bearing and gearbox fault experiments than one-stage those. In addition, it is also found that as the noise intensity and the number of iterations increase, the amplitude at the fault characteristic frequency tends to the peak value and then falls into a saturation value. Finally, compared with empirical mode decomposition (EMD), the proposed method can amplify the amplitude at the fault characteristic frequency beyond 1000 times in bearing and gearbox fault experiments. It was concluded that the proposed method has obvious advantages in extracting weak fault characteristics of machinery submerged by strong background noise.

High-performance deep-sea long-range underwater acoustic communication: Deconvolved conventional beamforming based approach

To address the challenges posed by large propagation loss and severe multipath delay spread, while aiming to improve communication rates in long-range underwater acoustic (UWA) communication, this paper introduces a novel approach: multi-beam (MB) Orthogonal Frequency Division Multiplexing (OFDM) based on deconvolved conventional beamforming (dCv). The proposed method establishes a signal processing framework for UWA channel receivers, wherein received signals corresponding to each angle beam are isolated and concentrated using the dCv technique. Subsequently, the separated signals from each angle undergo maximal-ratio combining (MRC) channel equalization followed by demodulation. Compared to conventional single-beam (SB) processing methods, the proposed approach capitalizes on multipath diversity gain achieved by combining MB outputs originating from various arrival angles. Moreover, employing dCv processing demonstrates superior performance compared to popularly employed beamforming techniques, especially when dealing with paths arriving from closely aligned angles. Additionally, the array beamforming utilized significantly enhances the signal-to-noise ratio (SNR) of each beam output, mitigating the elevated error rates in channel estimation associated with combining low SNR signals received from individual elements. Simulation results utilizing BELLHOP and experimental data from the South China Sea showcase notably improved bit error rate (BER) performance for the proposed method compared to SB equalization, MB-MRC equalization based on conventional beamforming (CBF), minimum variance distortionless response (MVDR), and worst-case performance optimization (WCPO), as well as standard MRC equalization techniques. The proposed receiver achieves error-free decoding results at a communication distance of 80 km with a data rate of 247 bps.

Microseismic activity characteristics and range evaluation of hydraulic fracturing in coal seam

Accurately evaluating hydraulic fracturing (HF) activity characteristics and impact range is critical to optimizing the HF scheme design. This study proposes an adaptive threshold peak detection method and a wavelet transform-support vector machine (WT-SVM) polarity analysis method based on microseismic (MS) monitoring data from a coal mine in Guizhou, China. The results show that the adaptive threshold peak detection method effectively improves the accuracy and stability of the P-wave initial arrival pickup for low signal-to-noise ratio (SNR) signals. The main frequency band of the MS signal of HF is focused on 150–300 Hz. The improved particle swarm optimization algorithm was used by introducing a random-weight strategy to determine the high-density impact areas of the HF within approximately 20 m on both sides of the fracturing borehole. Density cloud map analysis revealed a small area of the blank zone at the junction of the fracturing section, which provided a basis for fracturing parameter design and scheme optimization. The directional characteristics of the MS waveform were quantitatively described by polarity analysis, where the dip angle distribution ranged from 39° to 49°, the strike angle ranged from 61° to 74°, the slip angle was concentrated near 44°, and the stretch angle ranged from 61° to 74°. The WT-SVM proposed in this study overcame the effects of low SNR signals and the limitations of a small-area station network; 62% of the shear ruptures and 38% of the tensile ruptures of the HF were obtained. Shear cracks were the primary expression of the HF rupture type.

Coherent DOA Estimation Algorithm with Co-Prime Arrays for Low SNR Signals

The Direction of Arrival (DOA) estimation of coherent signals in co-prime arrays has become a popular research topic. However, traditional spatial smoothing and subspace algorithms fail to perform well under low signal-to-noise ratio (SNR) and small snapshots. To address this issue, we have introduced an Enhanced Spatial Smoothing (ESS) algorithm that utilizes a space-time correlation matrix for de-noising and decoherence. Finally, an Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm is used for DOA estimation. In comparison to other decoherence methods, when the SNR is −8 dB and the number of snapshots is 150, the mean square error (MSE) of the proposed algorithm approaches the Cramér–Rao bound (CRB), the probability of resolution (PoR) can reach over 88%, and, when the angular resolution is greater than 4°, the estimation accuracy can reach over 90%.

Improved Direction of Arrival Estimation using Multiple Signal Classification (MUSIC) Algorithm with Decomposition and Normalization

It is important to determine the direction of arrival (DoA) of targets in various applications such as radar and sonar. Multiple Signal Classification (MUSIC), Estimation of Signal Parameters with Rotational In variance Technique (ESPRIT), and Weighted Subspace Fitting (WSF) are subspace-based methods that can be used to improve DoA estimation. MUSIC is effective for high-resolution, uncorrelated signals, but may struggle in cases where there are two nearby targets with a low signal-to-noise ratio (SNR). The goal of this research is to improve the performance of the MUSIC algorithm for DoA estimation with low SNR signals. The proposed solution involves decomposing and normalizing the signal during transmission. Simulations were conducted to test the modified procedure with MUSIC algorithm for DoA estimation, and it was found that received signal power improved though there is noisy environment as well as system can detect more number of targets. The proposed technique of decomposition and normalization could also be applied in other areas such as WiFi communication, autonomous vehicles and biomedical signal and image processing etc.

An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection

Synthetic aperture radar (SAR) is essential for obtaining intelligence in modern information warfare. Wideband chirp signals with a low signal-to-noise ratio (SNR) are widely used in SAR. Intercepting low-SNR wideband chirp signals is of great significance for anti-SAR reconnaissance. Digital channelization technology is an effective means to intercept wideband signals. The existing digital channelization methods have the following problems: the contradiction of reception blind zone and signal spectrum aliasing, high computational complexity, and low estimating accuracy for chirp signals with a low SNR. This paper proposes a non-critical sampling digital channelized receiver architecture to intercept chirp signals. The receiver architecture has no blind zone in channel division and no aliasing of signal spectrum in the channel, which can provide reliable instantaneous frequency measurements. An adaptive threshold generation algorithm is proposed to detect signals without prior information. In addition, an improved instantaneous frequency measurement (IFM) algorithm is proposed, improving low SNR chirp signals’ frequency estimation accuracy. Moreover, a simple channel arbitration logic is proposed to complete the cross-channel combination of wideband signals. Simulations show that the proposed receiver architecture is reliable and robust for low SNR and wideband chirp signal detection. When the input SNR is 0 dB, the absolute frequency root-mean-square error (RMSE) of bandwidth and the center frequency is 0.57 MHz and 1.05 MHz, respectively. This frequency accuracy is great for radio frequency (RF) wideband systems.

FPGA-Based Pulse Compressor for Ultra Low Latency Visible Light Communications

Visible Light Communication (VLC) represents an emerging technology where a short-range data connection is obtained by modulating the energy radiated by Light Emitting Diodes (LEDs) at frequencies from a few kHz up to hundreds of MHz. The bandwidth/distance performance of such links is a compromise related to the available Signal-to-Noise ratio (SNR). At present, VLC links with bandwidth beyond the Gb/s and distance limited to a few cm or distances up to 100 m but data rates of a few kb/s have been demonstrated. Chirp coding with pulse compression is a well-known technique capable of recovering useful data from low SNR signals, widely employed, for example, in radar. In spite of the possible advantages, its application in VLC has never been investigated. Unfortunately, the pulse compressor is quite calculation-intensive, and only devices like Field-Programmable-Gate-Arrays (FPGAs) can support a low-latency real-time implementation. In this paper we demonstrate a real-time VLC link based on chirp coding and pulse compression coded in FPGA. For example, a chirp with bandwidth and length of 1.7 MHz and 17.92 µs, respectively, is demonstrated to support a link at 1.56 Mb/s over 2.8 m distance and a latency below 40 µs. Moreover, the communication-distance increase achievable by chirps of increasing temporal length is demonstrated and compared to the theoretical background.

Multimodal attention-based deep learning for automatic modulation classification

Wireless Internet of Things (IoT) is widely accepted in data collection and transmission of power system, with the prerequisite that the base station of wireless IoT be compatible with a variety of digital modulation types to meet data transmission requirements of terminals with different modulation modes. As a key technology in wireless IoT communication, Automatic Modulation Classification (AMC) manages resource shortage and improves spectrum utilization efficiency. And for better accuracy and efficiency in the classification of wireless signal modulation, Deep learning (DL) is frequently exploited. It is found in real cases that the signal-to-noise ratio (SNR) of wireless signals received by base station remains low due to complex electromagnetic interference from power equipment, increasing difficulties for accurate AMC. Therefore, inspired by attention mechanism of multi-layer perceptron (MLP), AMC-MLP is introduced herein as a novel AMC method for low SNR signals. Firstly, the sampled I/Q data is converted to constellation diagram, smoothed pseudo Wigner-Ville distribution (SPWVD), and contour diagram of the spectral correlation function (SCF). Secondly, convolution auto-encoder (Conv-AE) is used to denoise and extract image feature vectors. Finally, MLP is employed to fuse multimodal features to classify signals. AMC-MLP model utilizes the characterization advantages of feature images in different modulation modes and boosts the classification accuracy of low SNR signals. Results of simulations on RadioML 2016.10A public dataset prove as well that AMC-MLP provides significantly better classification accuracy of signals in low SNR range than that of other latest deep-learning AMC methods.

Improved time domain synchronous averaging based on the moving interpolation and kurtosis criterion searching

The fault diagnosis of rotating machinery involves revealing the possible faults in advance to reduce dispensable breakdowns; the difficulty of this lies in identifying the periodic features of rotating machines contaminated by background noise. Time-domain synchronous averaging (TSA) has been studied to eliminate random noise in signals. However, TSA sometimes cannot extract useful signals more accurately because of the accumulative phase error caused by the discrete sampling process and the difficulties in obtaining accurate prior information. Hence, the moving interpolation and Kurtosis searching criterion are used for more accurate extraction of harmonics and transient impacts. Also, an improved compensation algorithm based on moving interpolation is proposed to overcome the amplitude attenuation caused by cumulative phase error for low signal to noise ratio (SNR) signal. To determine some parameters in the algorithm such as the number of periods and the time delay of windows which depend on a priori information relevant to the fault period, a searching method for the prior information in vibration signals including transient impacts and harmonics with Kurtosis and minimum mean square error criterion is proposed to optimize the algorithm in the process of feature extraction. Finally, the improved TSA (ITSA) is applied to extracting the fault features in a real factory, and the performance of fault feature extraction in low SNR signal conditions with the ITSA has been enhanced effectively.

DOA Estimation Method Based on EMD and MUSIC for Mutual Interference in FMCW Automotive Radars

Mutual interference in automotive radar will reduce the signal-to-noise ratio (SNR) of the received signals. It is difficult to accurately distinguish the signal subspace and the noise subspace for low SNR signals, which will degrade the direction of arrival (DOA) estimation performance of the multiple signal classification (MUSIC) algorithm. To solve this problem, this letter proposes a DOA estimation method for automotive radar based on empirical mode decomposition (EMD) and MUSIC in the case of interference. In our method, the interference signals are suppressed before the DOA estimation. The received signals of the receiving antennas affected by the interference are decomposed into several components by using EMD, and then the interference signals in the interference components are suppressed. Finally, all the received signals are used for the DOA estimation. The results of the simulation and field experiments show that the proposed method can obtain an accurate DOA estimation when the received signals contain the interference signals, and the performance of the proposed method is better than the existing DOA estimation algorithms.

High-contrast imaging based on wavefront shaping to improve low signal-to-noise ratio photoacoustic signals using superpixel method**Project supported by the National Key Research and Development Program of China (Grant No. 2017YFB1104500), the Beijing Natural Science Foundation, China (Grant No. 7182091), the National Natural Science Foundation of China (Grant No. 21627813), and the Research Projects on Biomedical Transformation of China-Japan Friendship Hospital (Grant No. PYBZ1801).

Photoacoustic (PA) imaging has drawn tremendous research interest for various applications in biomedicine and experienced exponential growth over the past decade. Since the scattering effect of biological tissue on ultrasound is two- to three-orders magnitude weaker than that of light, photoacoustic imaging can effectively improve the imaging depth. However, as the depth of imaging further increases, the incident light is seriously affected by scattering that the generated photoacoustic signal is very weak and the signal-to-noise ratio (SNR) is quite low. Low SNR signals can reduce imaging quality and even cause imaging failure. In this paper, we proposed a new wavefront shaping and imaging method of low SNR photoacoustic signal using digital micromirror device (DMD) based superpixel method. We combined the superpixel method with DMD to modulate the phase and amplitude of the incident light, and the genetic algorithm (GA) was used as the wavefront shaping algorithm. The enhancement of the photoacoustic signal reached 10.46. Then we performed scanning imaging by moving the absorber with the translation stage. A clear image with contrast of 8.57 was obtained while imaging with original photoacoustic signals could not be achieved. The proposed method opens new perspectives for imaging with weak photoacoustic signals.

High-contrast photoacoustic imaging through scattering media using correlation detection of adaptive time window

Photoacoustic imaging has the advantages of high contrast and deep imaging depth. However, with the increasing of imaging depth, the signal-to-noise ratio (SNR) of the detected signal decreases, due to the light scattering that seriously affects the recovery image quality. In this paper, we experimentally demonstrated that higher contrast photoacoustic imaging was achieved using photoacoustic wavefront shaping technology in the presence of light scattering and low SNR signals. The imaging contrast is improved from 1.51 to 5.30. More importantly, we propose a dynamic time window method for the photoacoustic signal extraction algorithm, named correlation detection of adaptive time window, which further improves the contrast of photoacoustic imaging to 9.57. Our method effectively improves the contrast of photoacoustic imaging through scattering media.

Photoacoustic Signal Enhancement: Towards Utilization of Low Energy Laser Diodes in Real-Time Photoacoustic Imaging.

In practice, photoacoustic (PA) waves generated with cost-effective and low-energy laser diodes, are weak and almost buried in noise. Reconstruction of an artifact-free PA image from noisy measurements requires an effective denoising technique. Averaging is widely used to increase the signal-to-noise ratio (SNR) of PA signals; however, it is time consuming and in the case of very low SNR signals, hundreds to thousands of data acquisition epochs are needed. In this study, we explored the feasibility of using an adaptive and time-efficient filtering method to improve the SNR of PA signals. Our results show that the proposed method increases the SNR of PA signals more efficiently and with much fewer acquisitions, compared to common averaging techniques. Consequently, PA imaging is conducted considerably faster.

A Complexity-Based Approach for the Detection of Weak Signals in Ocean Ambient Noise

There are numerous studies showing that there is a constant increase in the ocean ambient noise level and the ever-growing demand for developing algorithms for detecting weak signals in ambient noise. In this study, we utilize dynamical and statistical complexity to detect the presence of weak ship noise embedded in ambient noise. The ambient noise and ship noise were recorded in the South China Sea. The multiscale entropy (MSE) method and the complexity-entropy causality plane (C-H plane) were used to quantify the dynamical and statistical complexity of the measured time series, respectively. We generated signals with varying signal-to-noise ratio (SNR) by varying the amplification of a ship signal. The simulation results indicate that the complexity is sensitive to change in the information in the ambient noise and the change in SNR, a finding that enables the detection of weak ship signals in strong background ambient noise. The simulation results also illustrate that complexity is better than the traditional spectrogram method, particularly effective for detecting low SNR signals in ambient noise. In addition, complexity-based MSE and C-H plane methods are simple, robust and do not assume any underlying dynamics in time series. Hence, complexity should be used in practical situations.

Optimal Multinode Sensing in a Malicious Cognitive Radio Network

Spectrum sensing is an essential function in cognitive radio systems for dynamic spectrum access. Multinode sensing is a technique being used in cognitive radio networks to enhance the sensing performance using space diversity concept. The challenges in multinode spectrum sensing are the prediction of signal status in multiple frequency bands in a low signal-to-noise ratio (SNR) regime and sensing reliability. The weighted gain combining (WGC) and the equal gain combining are the two soft decision cooperative sensing techniques being used frequently in literature. In this paper, we introduce weighted gain cooperative sensing using differential evolution (DE) and adjusted box-plot methods to exalt the sensing reliability together with the sensing performance. The main advantage of the WGC method using DE is that it can generate optimal weights independent of received signal characteristics, which is an indispensable condition to realize the system in real time. The proposed optimal cooperative sensing method with entropy and cyclic features enhances the sensing performance, and it is less severe to noise uncertainties compared with the traditional sensing methods. It can detect the low SNR signals up to $-$ 24 dB at desired sensing performance ( $P_{f} = 0.1$ and $P_{d} = 0.9$ ) with a frame size of 256 and using five nodes in cooperation. It is a significant improvement for IEEE 802.22 WRAN systems, which work under low SNR regime.

Adaptive Algorithm for Determination of Optimal Wavelet Decomposition Level Based on Jarque-Bera Test

Additive Gauss white noise is one of the most commonly observed interferences in practical engineering applications. This paper proposed an algorithm for the adaptive determination of the optimal wavelet decomposition level based on Jarque-Bera test in efforts to solve the filtering problem of additive white Gaussian noise signal. By, The optimal decomposition level of wavelet is determined by testing the white noise which was realized by calculating skewness (S) and kurtosis (K) of the parameters. With signal-to-noise ratio (SNR) as the measurement index, simulation results show that the presented algorithm have higher accuracy, and better filtering effect on low SNR signals compared with nonparametric test methods.

Multi-stable stochastic resonance and its application research on mechanical fault diagnosis

It is difficult to extract the fault features of a rotating machine via vibration analysis due to interference from background noise. Stochastic resonance (SR), used as a method of utilising noise to amplify weak signals in nonlinear dynamical systems, can detect weak signals overwhelmed in the noise. However, the detection effect of current SR methods is still unsatisfactory. To further increase the output signal-to-noise ratio (SNR) and improve the detection effect of SR, the present study proposes an improved SR method with a multi-stable model for identifying the defect-induced rotating machine faults by analysing the influence relationship between the resonance model and the resonance effect. Due to the structural characteristics of three potential wells and two barriers, the proposed resonance model can not only further amplify weak signals, but also convert into a monostable model, a bistable model or a tristable model. This result is achieved by adjusting system parameters and thus obtaining a better matching of the input signals and resonance models. Therefore, the multi-stable SR method, combined with the characteristics of the multi-stable model, can both increase the output SNR and improve the detection effect and also detect the low SNR signals and enhance the processing capability of SR for weak signals. Finally, the proposed method is applied to a gearbox fault diagnosis in a rolling mill in which two local faults located in the big gear and the pinion, respectively, are found successfully. It can be concluded that multi-stable SR method has practical value in engineering.

The Research on Bispectrum Detection of Underwater Target in Frequency Domain

As the non-Gaussianity of ship-radiated noise reduces fast when the Signal-to-Noise Ratio (SNR) becomes low, a bispectrum detector in the frequency domain is proposed to ease the problem. First, FFT method is applied on the received data to calculate the power spectrum. Second, the non-Gaussianity of the power spectrum series is tested by Hinich-Wilson Gaussian Test rule. Last, the bispectrum detector based on non-Gaussianity is used to determine whether there are ship-radiated signals. The bispectrum detector in frequency domain is applied to detect simulated noise and real ship-radiated noise. The results are compared with the detector which is in the signal’s time domain. The comparison illustrates that the bispectrum detector based on the power spectrum series(in frequency domain) is much better in detecting low SNR signals, which is very valuable in far distance detection.

KOMPSAT-2 영상과 IKONOS/GEOEYE-1 영상의 밝기값 상호비교

최근 수질추정을 위한 고해상도 위성영상의 활용 가능성에 대한 관심이 증대되고 있다. 그러나 고해상도 위성영상에서의 수면지역의 낮은 SNR(Signal to Noise Ratio)과 밝기값 비선형성과 같은 방사학적 오차는 수질추정 정확도를 감소시키는 원인이 된다. 이에 따라 위성영상을 이용한 정확한 수질추정을 위해서는 방사학적 보정이 반드시 수행되어져야 하나 KOMPSAT-2 위성영상의 경우 밝기값과 입사광량간 변환관계식이 제공되지 않기 때문에 이러한 방사학적 보정에 어려움이 존재한다. 따라서 수질모니터링에 KOMPSAT-2 영상을 활용하기 위해서는 밝기값 비선형성과 내륙 수면지역의 잡음현상을 실험적으로 파악할 필요가 있다. 본 논문에서는 충분한 검보정을 통해 선형성이 보장되고 있는 IKONOS와 GeoEye-1 영상을 기준영상으로 사용하여 동일시기에 동일지역을 촬영한 KOMPSAT-2 영상과의 밝기값 상호비교를 수행하였다. 상호비교 결과, 기준영상에 비해 KOMPSAT-2 영상의 잡음은 다소 높게 나타나나 밝기값의 경향 및 잡음의 크고 작은 패턴은 정확하게 일치하였고 KOMPSAT-2 영상 내 잡음의 영향이 최소화되는 적절한 영역의 크기는 <TEX>$5{\times}5$</TEX>로 나타났다. 또한 모든 실험 영상에서 밝기값의 비선형성은 존재하지 않는 것으로 확인되었다. 실험결과는 KOMPSAT-2 영상이 클로로필 농도와 같은 수질인자 추정을 위해 사용될 수 있는 가능성을 보여준다. Recently, interest in potential for estimating water quality using high resolution satellite images is increasing. However, low SNR(Signal to Noise Ratio) over inland water and radiometric errors such as non-linearity of brightness value of high resolution satellite images often lead to accuracy degradation in water quality estimation. Therefore radiometric correction should be carried out to estimate water quality for high resolution satellite images. For KOMPSAT-2 images parameters for brightness value-radiance conversion are not available and precise radiometric correction is difficult. To exploit KOMPSAT-2 images for water quality monitoring, it is necessary to investigate non-linearity of brightness value and noise over inland water. In this paper, we performed brightness value comparison between KOMPSAT-2 images and IKONOS/GeoEye-1, which are known to show the linearity. We used the images obtained over the same area and on the same date for comparison. As a result, we showed that although KOMPSAT-2 images are more noisy;the trend of brightness value and pattern of noise are almost similar to reference images. The results showed that appropriate target area to minimize the impact of noise was <TEX>$5{\times}5$</TEX>. Non-linearity of brightness value between KOMPSAT-2 and reference images was not observed. Therefore we could conclude that KOMPSAT-2 may be used for estimation of water quality parameters such as concentration of chlorophyll.

Max Separation Clustering for Feature Extraction From Optical Emission Spectroscopy Data

This paper proposes max separation clustering (MSC), a new non-hierarchical clustering method used for feature extraction from optical emission spectroscopy (OES) data for plasma etch process control applications. OES data is high dimensional and inherently highly redundant with the result that it is difficult if not impossible to recognize useful features and key variables by direct visualization. MSC is developed for clustering variables with distinctive patterns and providing effective pattern representation by a small number of representative variables. The relationship between signal-to-noise ratio (SNR) and clustering performance is highlighted, leading to a requirement that low SNR signals be removed before applying MSC. Experimental results on industrial OES data show that MSC with low SNR signal removal produces effective summarization of the dominant patterns in the data.