Low SNR Conditions Research Articles

Performance Simulation of Underwater Acoustic Spread Spectrum Communication Based on LDPC Code

As we all know, the underwater acoustic channel is a time-varying, space-changing, and frequency-varying fading channel with limited bandwidth and severe noise interference. This paper studies and analyzes the main factors restricting the transmission of underwater acoustic channels, and establishes an underwater acoustic multipath time-varying fading channel. The channel coding adopts Quasi-Cyslic Low-Density Parity-Check Codes (QC-LDPC), combined with spread spectrum technology and high-order modulation technology, and utilizes Log-likelihood Ratios Belief Propagation (LLR-BP) is decoded, the simulation results show that LDPC codes have stronger error correction capabilities in underwater acoustic spread spectrum communication systems, ensuring that the system can operate reliably under low SNR conditions.

A Spectrogram Based Local Fluctuation Feature for Fault Diagnosis with Application to Rotating Machines

Rotating machines are one of the most common equipment in modern industry, the health condition of the equipment is closely linked to safety of workers and production effectiveness. Thus accurate and robust fault diagnostic approaches are vital to safety production. In practice, diagnostic accuracy is seriously affected by noises, especially in low signal-to-noise (SNR) ratio conditions, and the quality of fault features is positively link to the diagnosing accuracy. In consideration of distinguishable feature expression can improve diagnosing result and robust to wider range of experimental conditions, this paper presents a novel spectrogram based local fluctuation feature (SLFF) for low SNR conditions. Firstly, signals are transformed into spectrograms. Then a feature extracting window bank is established on spectrograms for SLFF. At last, a support vector machine (SVM) is applied as a fault classifier for evaluating the proposed feature. The proposed SLFF represents the basic spectral shape and variation which leads to robust and well distinguishable feature expression, the feature reveals the differences of spectral local variation trends between normal and fault types that can improve the discrimination under the influence of strong noises. The effectiveness of the proposed method has been proved experimentally in this paper.

Minimization of arc tangent function penalty for off-grid multi-source passive localization by using a moving array

A novel direct passive localization technique with a single moving array is proposed in this paper. By using the sparse representation of the array covariance matrix in spatial domain, the measurement is constructed by stacking the vectorized version of all the array covariance matrices at different observing positions, and the dictionary is composed of the steering vectors from the searching targets' positions to the observing positions. An off-grid method based on sparse recovery is presented. As one of the main contributions in this paper, we prove that, to minimize the alternative atan-sum function is equivalent to minimize the original 0-norm. This method also works more robustly at conditions of low SNR, and fewer observing positions are needed than in the traditional ones. The simulation experiments verify the promises of the proposed algorithm.

How Much Benefit Can Dynamic Frequency Scaling Bring to WiFi?

Dynamic frequency scaling (DFS) is a state-of-the-art power-saving technique. Various DFS-based WiFi schemes have been proposed for power saving. These schemes demonstrated the power-saving feasibility of DFS via hardware implementation or simulation. This paper is the first that proposes a general theoretical framework to evaluate the performance of these schemes, where we use Queuing theory to analyze the system throughput and use Semi-Markov theory to quantify the power consumption. In addition, we adopt the energy efficiency (i.e., the throughput per energy cost) to compare the gains of these schemes. This efficiency measure can be used to make a trade-off between system throughput and power consumption and therefore help us choose appropriate parameter settings. Extensive simulations verify that our theoretical model is very accurate and our theoretical results well match with universal software radio peripheral (USRP) experiment results. Our study shows that DFS can greatly improve the energy efficiency of WiFi networks even under low SNR conditions; for example, when SNR = 9.7 dB (i.e., the basic requirement for decoding packets in WiFi), the improvement is around 25 percent for 802.11b at rate 11 Mb/s and 16 percent for 802.11 ac at rate 1300 Mb/s. Our study also shows that DFS can be well integrated with other commonly used power-saving mechanisms to improve energy efficiency further.

Robust and Efficient DOA-Steered Adaptive MVDR-FROST Beamforming Model for Multi-source Low SNR Environment

In the last few years, the demand of robust beamforming techniques has been increased significantly to serve numerous purposes. However, beamforming under low Signal to Noise Ratio (SNR) condition has always been a challenging task, which becomes more tedious in case of multiple sources. On the other hand, there are numerous factors such as steering vector error or similar estimation error problems that confine the efficiency of major beamforming techniques. Classical methods employ Eigen-Value Decomposition (EVD) and Singular Value Decomposition (SVD) over covariance matrix of the received signal; however their efficiency is limited under low SNR and multi-source condition, and perform inferiorly in case of steering vector errors. Such approaches are limited to achieve optimal Direction-of-Arrival (DOA) estimation that degrades the overall performance of the beamformer. Considering all these limitations, in this research, the focus is made on designing a robust DOA estimation model, followed by a novel beamformer for 8-element Uniform Linear Array (ULA). The simulation work consists two parts DOA estimation and beamforming. Unlike classical MUSIC and ESPRIT algorithms popular for DOA estimation our proposed model exploits the propagation method based subspace method where at first covariance matrix is obtained for the received signal, which is followed by normalization and Cross-Correlation matrix estimation. The obtained correlation matrix is processed for Generalized Eigen-Value Decomposition (GEVD) that efficiently obtained (orthogonal) subspace and SOI separation to yield DOA information. This approach enables more efficient DOA estimation even under low-SNR and steering vector error conditions. After obtaining DOA information, a novel adaptive beamformer is designed by using MVDR and FROST in parallel. DOA information from proposed model is given as input to the adaptive MVDR FROST beamforming model. Exploiting dynamic Signal-to-Interference ratio (SINR) by each beamformer our proposed method introduces an adaptation model that intends to achieve higher SINR value. MATLAB 2018a based simulation confirmed the robustness of the proposed model in terms of DOA estimation and beamforming results.

Deep Learning for Binaural Sound Source Localization with Low Signal-to-noise Ratio

A novel deep learning (DL) method is proposed for binaural sound source localization with low SNR. Firstly, the binaural sound signals are decomposed into several channels by using Gammatone filter. Secondly, the 4 feature parameters of Head-related Transfer Function, interaural time difference (ITD), interaural coherence (IC), interaural level difference (ILD), and interaural phase difference (IPD) are extracted. Thirdly, ITD and IC go through a Deep Belief Network (DBN) to determine the quadrant of the sound source and reduce the positioning range. Then, ITD, IC, ILD, and IPD go through a Deep Neural Network (DNN) to obtain the azimuthal angle within 90 degrees. Experimental results show that the proposed algorithm can solve the front-back confusion, and obtain a superior performance with lower complexity and higher precision under low SNR conditions.

Dim and Small Target Detection in Multi-Frame Sequence Using Bi-Conv-LSTM and 3D-Conv Structure

Infrared dim and small target detection is widely used in military and civil fields. Traditional methods in that application rely on the local contrast between the target and background for single-frame detection. On the other hand, those algorithms depend on the motion model with fixed parameters for multi-frame association. For the great similarity of gray value and the dynamic changes of motion model parameters in the condition of low SNR and strong clutter, those methods possess weak robustness, low detection probability, and high false alarm rate. In this paper, an infrared video sequences encoding and decoding model based on Bidirectional Convolutional Long Short-Term Memory structure (Bi-Conv-LSTM) and 3D Convolutional structure (3D-Conv) is proposed, addressing the problem of high similarity and dynamic changes of parameters. For solving the problem of dynamic change in parameters, Bi-Conv-LSTM structure is used to learn the motion model of targets. And for the problem of low local contrast, 3D-Conv structure is adopted to extend receptive field in the time dimension. In order to improve the precision of detection, the Decoding part is divided into two different full connections with distinctive active function. Simulation results show that the trajectory detection accuracy of the proposed model is more than 90% under the condition of low SNR and maneuvering motion, which is better than traditional method of 80% in DB-TBD 20% in others. Real data experiment to illustrate that that our proposed method can detect small infrared targets of a low false alarm rate and high detection probability.

A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm

The modulation recognition method based on deep learning plays a significant role in the intelligent communication system. To further improve the recognition rate, especially in the case of small samples with a low signal-to-noise ratio, this paper proposes a new modulation recognition method based on flying fish swarm algorithm. First, Short-Time Fourier Transform, Choi-Williams Distribution, and Cyclic Spectrum are combined to complete multi-channel signal processing. Second, AlexNet, VGGNet, GoogLeNet, and ResNet are transferred to realize feature extraction. Third, the support vector machine classifies the modulations after dimension reduction and feature fusion. Finally, the flying fish swarm is proposed to optimize the signal processing methods, the types of networks, the layers of networks, the dimensions of features, and the parameters of the support vector machine. The method can accurately recognize BPSK, QPSK, OQPSK, 8PSK, 4ASK, QAM16, QAM32, and QAM64. The simulation results show that the average recognition rate of modulation is 94.5% at SNR of 0 dB and 84.7% at SNR of −4 dB. Besides, the proposed modulation recognition method possesses good robustness under low SNR conditions.

Propeller feature extraction of UUVs study based on CEEMD combined with symmetric correlation

In this paper, in view of the characteristic that UUV radiation noise is low and easily interfered by strong noise, the complementary Ensemble Empirical Mode Decomposition (CEEMD) combined with symmetric correlation processing is proposed, which can improve the extraction performance of UUV’s propeller features. First, the CEEMD decomposition combined with symmetric correlation processing was used to reduce the radiated noise of the target, then the signals after the noise reduction were demodulated and computed to obtain the DEMON spectrum, and finally features such as the rotational speed of the UUV’s propeller were extracted from the DEMON spectrum. The Sea trials signal processing results prove that the method has better noise suppression performance under low SNR conditions, and can clearly and comprehensively extract the DEMON information of the radiated noise, and then accurately extract the propeller features of the UUV. Compared to conventional demodulation techniques, this technique has a greater ability to suppress noise and does not require manual selection of the modulated frequency band.

An Efficient Improved OGWSBI Algorithm for Accurate Off‐Grid DOA Estimation of Coherent Signals

The performance of the weighted sparse Bayesian inference (OGWSBI) algorithm for off‐grid coherent DOA estimation is not satisfactory due to the inaccurate weighting information. To increase the estimation accuracy and efficiency, an improved OGWSBI algorithm based on a higher‐order off‐grid model and unitary transformation for off‐grid coherent DOA estimation is proposed in this paper. Firstly, to reduce the approximate error of the first‐order off‐grid model, the steering vector is reformulated by the second‐order Taylor expansion. Then, the received data is transformed from complex value to real value and the coherent signals are decorrelated via utilizing unitary transformation, which can increase the computational efficiency and restore the rank of the covariance matrix. Finally, in the real field, the steering vector higher‐order approximation model and weighted sparse Bayesian inference are combined together to realize the estimation of DOA. Extensive simulation results indicate that under the condition of coherent signals and low SNR, the estimation accuracy of the proposed algorithm is about 50% higher than that of the OGWSBI algorithm, and the calculation time is reduced by about 60%.

Speech enhancement by LSTM-based noise suppression followed by CNN-based speech restoration

Single-channel speech enhancement in highly non-stationary noise conditions is a very challenging task, especially when interfering speech is included in the noise. Deep learning-based approaches have notably improved the performance of speech enhancement algorithms under such conditions, but still introduce speech distortions if strong noise suppression shall be achieved. We propose to address this problem by using a two-stage approach, first performing noise suppression and subsequently restoring natural sounding speech, using specifically chosen neural network topologies and loss functions for each task. A mask-based long short-term memory (LSTM) network is employed for noise suppression and speech restoration is performed via spectral mapping with a convolutional encoder-decoder network (CED). The proposed method improves speech quality (PESQ) over state-of-the-art single-stage methods by about 0.1 points for unseen highly non-stationary noise types including interfering speech. Furthermore, it is able to increase intelligibility in low-SNR conditions and consistently outperforms all reference methods.

Automated weak signal extraction of hyperspectral Raman imaging data by adaptive low‐rank matrix approximation

AbstractHyperspectral Raman imaging has emerged as a promising spectroscopic tool that can provide spatial and molecular information of the sample in a label‐free and noninvasive manner, which is very suitable to the biological and biomedical research. However, the intrinsically weak Raman scattering effect results in the low signal quality of the measured Raman spectra, which has largely limited the application of Raman imaging. In this paper, we develop an adaptive low‐rank matrix approximation method to automatically extract the signal from the noisy hyperspectral Raman imaging data. After spike removal, the hyperspectral Raman imaging data are decomposed into a linear combination of submatrices by singular value decomposition. Next, the submatrices are classified into positive and negative groups according to the SNR contribution. The negative group, reflecting the instrumental noise, is discarded, and the positive group is used to reconstruct the denoised signal. We prove on the simulated data that this algorithm can significantly decrease the normalized mean noise error from 34.35% down to 1.9%. Such a strong denoising performance enables it to efficiently extract the signal from the noisy hyperspectral Raman imaging data, especially under the low SNR condition. We finally apply this algorithm to the fast speed Raman imaging of HeLa cell and surprisingly find that the slight difference of the spectra can be differentiated after signal extraction. This algorithm offers a promising tool in the Raman imaging and also can be extended to other spectroscopic imaging platforms such as fluorescent and IR spectroscopy.

MIMO Based Cognitive Radio Network with Efficient Spectrum Utilization

This paper presents an energy-efficient power control scheme for Multi-Input–Multi-Output (MIMO) based multi-channel cognitive radio networks (CRNs) with adaptive mode selection protocol. Cognitive radio device operates in the temporal domain to utilize spectrum holes and MIMO system operates in the spatial domain. Thus CRN in collaboration with MIMO can produce a significant improvisation for spectrum utilization, and therefore, the integration of MIMO and CRN has received widespread research interest. To address the challenges of the collaboration of MIMO and CRN, we propose a MIMO based CRN network in this paper, in which basic CRN is modified with the QPSK transmission technique having four antenna elements per primary users (PU) and secondary users (SU). Similarly, MIMO significantly improves channel capacity particularly in SU transmission under low SNR condition, but issues like several antenna elements and specific antenna selection issues increase the computational complexity and energy consumption. The proposed network protocol strives to improve communication stability and rate of throughput via selecting the MIMO operating parameters such as multiplexing, diversity, or hybrid multiplexing-diversity gains. Specifically, we designed transmission power distribution along with an antenna selection scheme for enhancing the energy efficiency of CRN’s under the influence of the maximum interference caused by the SU to PU, utmost power transmission from SU, and the least rate of transmission through the SU link. Finally, the simulation results are compared with basic CRN alone using the BPSK scheme to evaluate and validate the proposed scheme.

Research on Speech Enhancement Based on Deep Neural Network

The traditional single-channel speech enhancement algorithm has many unreasonable assumptions, which limit the performance of the algorithm. The speech enhancement based on deep neural network can effectively eliminate the problems such as “music noise” in the traditional methods, thus achieving better results than the traditional single-channel speech enhancement. However, the DNN-based speech enhancement model does not perform well at low SNR. In order to improve the robustness of the model, we used voice activity detection (VAD) to process the training data to obtain a new VAD-DNN speech enhancement model. We added 100 kinds of noise in the training set to improve the ability of the model to deal with the unseen noise. At the same time, in order to prevent the occurrence of overfitting problem, we introduced dropout technology to process the network, and improved the generalization ability of the model by using noise awareness training (NAT). Through experiments, we found that the speech enhancement model based on VAD-DNN improved PESQ index by 0.02 and STOI index by 0.01 on average under the condition of low SNR.

Blind recognition of MIMO-SFBC based on Symbolic eigenvalue*

In the blind recognition of air frequency block code signals, a blind recognition algorithm of MIMO-SFBC based on symbolic eigenvalue was proposed to solve the problems of poor recognition performance and large number of samples under the condition of low SNR. According to the symbol correlation characteristics of different air frequency block codes in frequency domain, the eigenvector sequences of different air frequency block codes are derived, symbolic eigenvalue are estimated by binary hypothesis testing, and different coding types are distinguished by decision tree classification recognition algorithm. Simulation results show that the algorithm does not need prior information such as modulation prediction, and has good recognition performance under low SNR and small sample conditions, and can be applied to engineering fields such as cognitive radio.

Time-correlated single molecule localization microscopy enhances resolution and fidelity

Single-molecule-localization-microscopy (SMLM) enables superresolution imaging of biological samples down to ~ 10–20 nm and in single molecule detail. However, common SMLM reconstruction largely disregards information embedded in the entire intensity trajectories of individual emitters. Here, we develop and demonstrate an approach, termed time-correlated-SMLM (tcSMLM), that uses such information for enhancing SMLM reconstruction. Specifically, tcSMLM is shown to increase the spatial resolution and fidelity of SMLM reconstruction of both simulated and experimental data; esp. upon acquisition under stringent conditions of low SNR, high acquisition rate and high density of emitters. We further provide detailed guidelines and optimization procedures for effectively applying tcSMLM to data of choice. Importantly, our approach can be readily added in tandem to multiple SMLM and related superresolution reconstruction algorithms. Thus, we expect that our approach will become an effective and readily accessible tool for enhancing SMLM and superresolution imaging.

Dictionary Learning for Channel Estimation in Hybrid Frequency-Selective mmWave MIMO Systems

Exploiting channel sparsity at millimeter wave (mmWave) frequencies reduces the high training overhead associated with the channel estimation stage. Compressive sensing (CS) channel estimation techniques usually adopt the (overcomplete) Fourier transform matrix as sparsifying dictionary. This may not be the best choice when considering hardware impairments in practical arrays. We propose two dictionary learning (DL) algorithms to learn the best sparsifying dictionaries for channel matrices from observations obtained with practical hybrid frequency-selective mmWave multiple-input-multiple-output (MIMO) systems. First, we optimize the combined dictionary, i.e., the Kronecker product of transmit and receive dictionaries, as it is used in practice to sparsify the channel matrix. This stage operates as a calibration phase, since all the hardware imperfections are embedded into the learnt dictionaries. Second, considering the different array structures at the transmitter and receiver, we exploit separable DL to find the best transmit and receive dictionaries. Once the channel is expressed in terms of the optimized dictionaries, various CS-based sparse recovery techniques can be applied for low overhead channel estimation. The effectiveness of the proposed DL algorithms under low SNR conditions has been corroborated via numerical simulations with different system configurations, array geometries and hardware impairments.

Seismic Random Noise Suppression by Using Adaptive Fractal Conservation Law Method Based on Stationarity Testing

Attenuating the random noise and improving the signal-to-noise ratio (SNR) for the seismic data are of great significance in industrial exploration. In recent years, fractal conservation law (FCL) has been proposed and applied to seismic random noise suppression successfully. However, in conventional FCL, the filtering parameter selection strategy is relatively simple and a fixed parameter set is always used for the whole seismic record. In addition, it is very difficult to make an excellent tradeoff in random noise attenuation and signal preservation only by fixed parameters especially under the low-SNR conditions. Thus, accurately recognizing the effective signals and adaptively choosing appropriate filtering parameters is a feasible approach to improve the performance of the conventional FCL. In this article, an adaptive FCL methodology is proposed by combining the seismic noise analyzing theory and stationarity testing techniques. It is known that the random noise and reflection signals have different properties in stationarity and thus, the signal and noise segments can be divided by stationarity testing. As a consequence, different filtering parameters can be adopted for signal and noise segments to achieve the noise suppression and signal preservation simultaneously. Synthetic and field data experiments demonstrate that the proposed method can remove the random noise from seismic record and effectively preserve the reflection events.

Speech Intelligibility for Cochlear Implant Users with the MMSE Noise-Reduction Time-Frequency Mask

Cochlear implants (CI) are the most successful electronic prostheses for human beings. They almost completely restore the communication capability in profound-to-severe hearing losses under quiet conditions. However, noisy scenarios still impose critical intelligibility limitations. Time-frequency masks have been widely used to improve signal to noise conditions (SNR) and, as a consequence, increase the speech intelligibility. The most studied methods in the literature are the Binary mask (BM) and the Wiener filter (WF), which were not originally designed for this application. This work analyzes the performance of the minimum mean square error (MMSE) magnitude estimator for increasing intelligibility in CI applications. Objective measures and psychoacoustic experiments with normal hearing volunteers and vocoded signals, as well as CI users indicate that the MMSE method outperfoms the intelligibility performance obtained by both BM and WF time-frequency masks, especially in low SNR conditions (SNR < 8 dB). This observation can be explained by the fact that CIs do not provide speech temporal-fine-structure information to the CI user, and that hard masks may suppress important speech information under low SNR. As a result, we provide strong indications that the MMSE magnitude-based time-frequency mask is more suitable to CI noise reduction applications, as compared to WF and BM, whenever the additional computation burden is tolerable by the CI processor.

Bistable stochastic resonance with linear amplitude response enhanced vector DOA estimation under low SNR conditions

Abstract This work demonstrates a superior vector DOA estimation results by linear amplitude response of stochastic resonance with bistable nonlinear model, especially under low SNR conditions. The pre-processing problem of classical intensity based vector DOA estimation method is theoretically analyzed with gain-phase uncertainties, which demonstrate a constraint of linear amplitude response with a certain phase shift for an unbiased estimate of the true azimuth. In this way, linear amplitude response stochastic resonance is parametric modeled with gain-phase constraint, and achieved by the matched stochastic resonance theory with a maximized output SNR and a steady phase lag of π/2. The linear relation between the input and the output amplitude is simulative analyzed under different SNR conditions, which reflect a good linearity with the input amplitude A0