Low Signal-to-noise Ratio Conditions Research Articles

Improved Vehicle Ranging Method for the IEEE 802.11p

In view of drawbacks in GNSS-dark areas such as forests, tunnel, etc., which cause position results inaccuracy, high-mobility positioning methods using IEEE 802.11p standard in vehicular ad hoc networks (VANETs) should be developed. Accuracy time of arrival (TOA) estimation based ranging model has attracted wide attention as one of the challenges for vehicular collision avoidance system. In this paper, we propose an efficient TOA or ranging estimation method using IEEE 802.11p short preamble in order to mitigate the effect of multipath vehicle measurements and low signal noise ratio (SNR).Firstly, the TOA estimation is performed using auto correlation and cross-correlation. Then, the approach to sum is presented for finding the accurate time offset. Simulation results, in the International Telecommunication Union Vehicle A (ITUA) channel and an additive white Gaussian noise (AWGN) channel, prove the superiority of proposed algorithm in low SNR conditions and multipath environments.

An EEMD‐Based Denoising Method for Seismic Signal of High Arch Dam Combining Wavelet with Singular Spectrum Analysis

Due to complicated noise interference, seismic signals of high arch dam are of nonstationarity and a low signal‐to‐noise ratio (SNR) during acquisition process. The traditional denoising method may have filtered effective seismic signals of high arch dams. A self‐adaptive denoising method based on ensemble empirical mode decomposition (EEMD) combining wavelet threshold with singular spectrum analysis (SSA) is proposed in this paper. Based on the EEMD result for seismic signals of high arch dams, a continuous mean square error criterion is used to distinguish high‐frequency and low‐frequency components of the intrinsic mode functions (IMFs). Denoised high‐frequency IMF using wavelet threshold is reconstructed with low‐frequency components, and SSA is implemented for the reconstructed signal. Simulation signal denoising analysis indicates that the proposed method can significantly reduce mean square error under low SNR condition, and the overall denoising effect is superior to EEMD and EEMD‐Wavelet threshold denoising algorithms. Denoising analysis of measured seismic signals of high arch dams shows that the performance of denoised seismic signals using EEMD‐Wavelet‐SSA is obviously improved, and natural frequencies of the high arch dams can be effectively identified.

Non-Contact Life Signal Extraction and Reconstruction Technique Based on MAE

Non-contact life signal extraction could be used in natural disaster rescue, health care and other fields. As the energy of respiration and heartbeat signals is extremely weak in reality, they are usually submerged in noise and clutter. As a result, the traditional life signal extraction algorithms always fail at low signal-to-noise ratio (SNR) conditions. This paper proposes a novel life signal extraction and reconstruction algorithm based on MTI-Autocorrelation-EEMD (MAE) so as to enhance the accuracy and stability of life signal detection at low SNR condition. Taking advantage of its strong robustness with regard to noise, this technique utilizes a moving target indicator (MTI) algorithm to eliminate the interference of fixed object clutter on the echo signal after pulse compression. Combined with the autocorrelation algorithm with stable detection performance, the precise location of the human body micro-motion signal can be determined. Through the integration of an ensemble empirical mode decomposition (EEMD) algorithm, the phase of human body micro-motion signal is adaptively decomposed, overcoming the problem of mode mixing so that the signals of respiration and heartbeat can be reconstructed in the time domain according to the mode judgement criteria. Extensive real data experiments illustrate the effectiveness and robustness of the proposed algorithm under low SNR.

High-Resolution Time of Arrival Estimation With Small Samples Considering Time-Varying Channel Fading Coefficient Correlation in OFDM

This paper proposes a new method using orthogonal frequency division multiplexing (OFDM) signals for time-of-arrival (TOA) estimation in complex environments, such as electromagnetic interference and short burst received signals. Under that circumstance, sometimes only a short period of signals that were seriously interfered [i.e., low signal-to-noise ratio (SNR) and small samples] can be received for estimation. However, the traditional algorithm would be faced with a decrease in estimation accuracy under that conditions. Due to the fact that the sparse reconstruction method can recover the original signal from a small number of observations with high probability, this paper proposes an OFDM TOA estimation algorithm based on it. The algorithm not only considers the correlation of the channel complex fading coefficients in the samples at adjacent moments but also considers the off-grid effect. Under the Bayesian learning framework, this correlation and off-grid parameter are introduced into the estimation process, which effectively improves the estimation accuracy under small samples and low SNR conditions. The proposed algorithm first constructs a sparse representation model based on the channel frequency response estimated by the OFDM signal physical layer protocol data unit and then introduce the correlation characterization matrix and the off-grid parameter and make probability assumptions for the noise vector, the off-grid parameter, and the sparse coefficient vector in the model. Finally, according to Bayesian inference, the expectation-maximization algorithm is used to solve the hyperparameters to achieve high-resolution estimation of TOA. The simulation results show that the proposed algorithm has a better estimation performance than the traditional algorithms and existing sparse reconstruction algorithms. In addition, its performance curve is closer to the Cramer-Rao bound at low SNR. At the same time, based on the universal software radio peripheral, the effectiveness of the proposed algorithm is verified by the actual OFDM signal.

Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks

Spectrum sensing is one of the most important and challenging tasks in cognitive radio. To develop methods of dynamic spectrum access, robust and efficient spectrum sensors are required. For most of these sensors, the main constraints are the lack of information about the primary user’s (PU) signal, high computational cost, performance limits in low signal-to-noise ratio (SNR) conditions, and difficulty in finding a detection threshold. This paper proposes a machine learning based novel detection method to overcome these limits. To address the first constraint, detection is achieved using cyclostationary features. The constraints of low SNR, finding detection threshold, and computational cost are addressed by proposing an ensemble classifier. First, a dataset is generated containing different orthogonal frequency-division multiplexing signals at different SNRs. Then, cyclostationary features are extracted using FFT accumulation method. Finally, the proposed ensemble classifier has been trained using the extracted features to detect PU’s signal in low SNR conditions. This ensemble classifier is based on decision trees and AdaBoost algorithm. A comparison of the proposed classifier with another machine learning classifier, namely, support vector machine (SVM), is presented, clearly showing that the ensemble classifier outperforms SVM. The results of the simulation also prove the robustness and superior efficiency of the detector proposed in this paper in comparison with a cyclostationary detector without machine learning as well as the classical energy detector.

Synchronization challenges in deep space communications

Deep space missions keep pushing for new frontiers affecting a wide spectrum of disciplines. To support the scientific achievements expected from new missions, communication technology is being pushed towards its limits [1]. A need to increase communication links data rate as well as to lower the operative signal-to-noise ratio (SNR) are identified. The adoption of advanced coding schemes such as turbo codes and low-density parity-check (LDPC) codes (e.g., Consultative Committee for Space Data Systems (CC-SDS) standards) allows receivers to operate at lower SNRs. However, in order to exploit the full potential of the coding gain, the receiver must be able to acquire and track a signal with a SNR much lower than expected in nominal conditions of state-of-the-art systems. The target operating point is given by the candidate LDPC codes [2], where the codeword error rate is set to WER ≤ 10−5, achieved at the bit energy to noise density ratio E b /N 0 ≥ 5.2 dB, ≥ 3.6 dB for LDPC(128,64) and LDPC(256,128), respectively. In [3] the first receiver bottleneck related with frame synchronization, a functionality required previous to channel decoding, was identified. Even though frame synchronization enhancements were proposed beyond standard correlation techniques [3], [4], [1], it was recommended to increase the synchronization word length in order to achieve the target performance. The recommendation was recently adopted by the CCSDS. In this work, the focus lies on the receiver synchronization stages (i.e., acquisition and tracking). Not only from a research standpoint, but also for the design of next generation Telemetry Tracking & Command (TT&C) transponders, it is of capital importance to understand the performance limitations of state-of-the-art deep space communications architectures, clearly identifying possible bottlenecks and the synchronization stages (i.e., acquisition and tracking) to be improved. Digital carrier and timing synchronization have been an active research field for the past three decades in applications such as satellite-based positioning or terrestrial wireless communications systems. In those scenarios, the limitations of standard delay, frequency, and phase-locked loop (delay-locked loop, frequency-locked loop (FLL), and phase-locked loop (PLL), respectively) architectures have been clearly overcome by Kalman filter (KF) based solutions [5], which provide an inherent adaptive bandwidth, robustness, flexibility, and an optimal design methodology. Despite the advances in the field, synchronization architectures for deep space communications links, implemented in current TT&C transponders, still rely on well-known conventional architectures, which may be insufficient if limits are pushed to extremely low SNR or harsh propagation conditions. With the advent of powerful software defined radio receivers and new system design rules, it is now possible to adopt new robust architectures that may enable going beyond the performance and reliability provided by legacy solutions.

Robust S1 and S2 heart sound recognition based on spectral restoration and multi-style training

Abstract Recently, we have proposed a deep learning based heart sound recognition framework, which can provide high recognition performance under clean testing conditions. However, the recognition performance can notably degrade when noise is present in the recording environments. This study investigates a spectral restoration algorithm to reduce noise components from heart sound signals to achieve robust S1 and S2 recognition in real-world scenarios. In addition to the spectral restoration algorithm, a multi-style training strategy is adopted to train a robust acoustic model, by incorporating acoustic observations from both original and restored heart sound signals. We term the proposed method as SRMT (spectral restoration and multi-style training). The experimental procedure in this study is described as follows: First, an electronic stethoscope was used to record actual heart sounds, and the noisy signals were artificially generated at different signal-to-noise-ratios (SNRs). Second, an acoustic model based on deep neural networks (DNNs) was trained using original heart sounds and heart sounds processed through spectral restoration. Third, the performance of the trained model was evaluated using the following metrics: accuracy, precision, recall, specificity, and F-measure. The results confirm the effectiveness of the proposed method for recognizing heart sounds in noisy environments. The recognition results of an acoustic model trained on SRMT outperform that trained on clean data with a 2.36% average accuracy improvement (from 85.44% and 87.80%), over clean, 20dB, 15dB, 10dB, 5dB, and 0dB SNR conditions; the improvements are more notable in low SNR conditions: the average accuracy improvement is 3.87% (from 82.83% to 86.70%) in the 0dB SNR condition.

Atmospheric turbulence detection by PCA approach

By detecting the severe meteorological situations on flight route, airborne weather radar (WXR) can ensure the safety of the aircraft and on-board personnel. Among these critical weather conditions, atmospheric turbulence is one of the main factors that affect flight safety. Atmospheric turbulence detection method that the current WXR adopts mainly is the pulse pair processing (PPP) method, which estimates Doppler spectrum width of weather target echo and compares it with a threshold to determine whether this weather target is turbulence or not. PPP method is simple and easy to implement, but the performance of this method under the condition of low signal-to-noise ratio (SNR) is poor. In this paper, we propose a new turbulence detection method based on the principal component analysis (PCA) approach. This new method uses PCA approach to preprocess the weather target echo and divides it into two parts: the principal component part as signal and the rest part as noise, so as to realize the de-noising function of PCA approach, and it is then combined with PPP method to estimate the spectrum width. Due to the good de-noising performance of PCA approach, this new method improves the detection performance of traditional PPP method especially under the condition of low SNR.

A novel demodulation method for rotating machinery based on time-frequency analysis and principal component analysis

Demodulation is a highly effective method for feature extraction of rotating machinery. Vibration/acoustic signal of rotating machinery carries substantial information that represents the mechanical equipment conditions. Therefore, the demodulation of vibration/acoustic signals is a key issue in fault diagnosis and target recognition. However, background noise enhances the difficulty of signal demodulation. This study proposes a novel demodulation method based on time-frequency analysis and principal component analysis (DPCA) to enhance demodulation performance under low signal-to-noise ratio (SNR). The periodic modulation wave signal is extracted by combining time–frequency analysis and principal component analysis (PCA). The effectiveness of the proposed method is verified by mean of simulation analysis and two real application cases. Firstly, monocomponent and multicomponent modulated simulation signals are used to test the performance of the proposed method. Secondly, the proposed method is applied to the vibration signals of the pump and acoustic signals of the propeller. Finally, the superiority of the proposed demodulation method is demonstrated by comparing the demodulation results with the kurtogram and cyclostationary analysis method under low SNR condition.

PSFM—A Probabilistic Source Filter Model for Noise Robust Glottal Closure Instant Detection

Accurate estimation of glottal closure instant (GCI) enables several pitch synchronous speech analysis, such as prosody modifications, glottal inverse filtering, and study of pathological speech. We propose a probabilistic source-filter model (PSFM) for voiced speech, where the source is modeled using the Bernoulli Gaussian distribution, which models the GCI locations and the all-pole filter coefficients are modeled using Gaussian distribution. The probability of GCIs at each speech sample is estimated using the Gibbs sampling. We propose a cost to estimate the exact GCI locations using the N-best dynamic programming. A key feature of the proposed PSFM is that it allows us to include the second-order statistics of the noise for estimating the GCI locations, thereby resulting in a noise robust GCI detection technique, although it has high computational complexity. Evaluation on archivable priority list actual-word database (APLAWD) database shows the proposed algorithm performs at par with the state-of-the-art GCI detection method on clean speech. However, when evaluated in noisy conditions using five types of noises at six different signal-to-noise ratio (SNR) levels, we observe that the proposed method performs better than the best of the existing GCI detection scheme, particularly at low SNR condition indicating the noise robustness of the proposed method.

Joint signal-to-noise ratio and source number estimation based on hierarchical artificial intelligence units

Accurately measuring the direction of arrival (DOA) is one of the most important issues in multiple sensor/antenna array monitoring scenarios. However, as a necessary parameter of almost all state-of-the-art DOA estimation methods, the source number is always hard to be determined by using the traditional Akaike information criterion (AIC) or minimum description length (MDL) methods, especially in low or very low signal-to-noise ratio (SNR) conditions. In this paper, we propose to sequentially estimate the SNR and source number in a novel data-driven manner by employing artificial intelligence (AI) techniques. Specifically, a simulated uniform linear array (ULA) dataset with different source numbers and different SNRs is first generated. With this dataset, the artificial neural network (ANN) regression unit for SNR prediction is built. Then, a hierarchical support vector machine (SVM) classification unit is constructed to estimate the source number. Finally, with these hierarchical AI units, the SNR and source number were estimated simultaneously in an iterative way. Experimental results illustrated that the proposed method can estimate the source number stably and reliably even in the low SNR condition.

Vibration phases estimation based on multi-channel interferometry for ISAL.

Target vibration introduces unexpected vibration phases in the echo signal of inverse synthetic aperture LADAR (ISAL). The vibration phases cause the imaging results to be defocused. How to estimate the vibration phases has always been one of the difficulties in ISAL imaging, and there has not been an effective solution. In this paper, two methods are proposed to estimate the vibration phases in different situations. The method based on multi-channel along-track interferometry (MCATI method) is for some cases that the target velocity vector is parallel to some baselines of ISAL. The method based on orthogonal interferometry (OI method) is for other cases that the target velocity vector is not parallel to any baseline of ISAL. In addition, a high signal to noise ratio (SNR) is essential for the MCATI and OI methods to get high estimation accuracy. Therefore, a large power-aperture product should be carefully considered in the ISAL system design. Furthermore, on the condition of low SNR, range cell accumulation is also helpful to reduce the influence of noise. Take the ISAL, which is utilized to image the satellite in 500km orbit, as an example, the estimation accuracy can satisfy the demand of synthetic aperture imaging with 0dB single-pulse SNR and 300 range cell accumulation. The proposed methods are verified with simulation and experiment.

Tracking and detecting highs maneuvering weak targets

Modern military targets as aircraft are able to perform high maneuvers due to their complex design. This ability of maneuvers includes sudden changes in acceleration and high-G turns that are not achievable from traditional military targets. Moreover, recent military targets generally have a low radar cross-section or low signal-to-noise ratio (SNR) profile, which makes the detection and tracking of those maneuvering targets a complicated dynamic state estimation problem. In this case, the track-before-detect filter (TBDF) that uses unthresholded measurements is considered as an effective method for tracking and detecting a single maneuvering target under low SNR conditions. Nevertheless, the performance of the algorithm will be affected by severe loss because of the mismatching of target model during maneuver. To resolve the target maneuvers, we propose an application of particle filtering, which depends on TBD. We employ the constant acceleration model and coordinate turn model. Our simulation results show that the detection and tracking of maneuvering target performance of TBDF has been improved using the proposed algorithm.

Periodic component analysis as a spatial filter for SSVEP-based brain–computer interface

BackgroundTraditional spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. New methodIn this paper, Periodic component analysis (πCA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The πCA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates. ResultsData from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results. Comparison with existing methodsThe experimental results show that πCA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions. ConclusionsThe results demonstrate that πCA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence πCA is a reliable and efficient alternative detection algorithm for SSVEP based brain–computer interface (BCI).

Highly accurate adaptive TOF determination method for ultrasonic thickness measurement

Determining the time of flight (TOF) is very critical for precise ultrasonic thickness measurement. However, the relatively low signal-to-noise ratio (SNR) of the received signals would induce significant TOF determination errors. In this paper, an adaptive time delay estimation method has been developed to improve the TOF determination’s accuracy. An improved variable step size adaptive algorithm with comprehensive step size control function is proposed. Meanwhile, a cubic spline fitting approach is also employed to alleviate the restriction of finite sampling interval. Simulation experiments under different SNR conditions were conducted for performance analysis. Simulation results manifested the performance advantage of proposed TOF determination method over existing TOF determination methods. When comparing with the conventional fixed step size, and Kwong and Aboulnasr algorithms, the steady state mean square deviation of the proposed algorithm was generally lower, which makes the proposed algorithm more suitable for TOF determination. Further, ultrasonic thickness measurement experiments were performed on aluminum alloy plates with various thicknesses. They indicated that the proposed TOF determination method was more robust even under low SNR conditions, and the ultrasonic thickness measurement accuracy could be significantly improved.

Bayesian Bistatic ISAR Imaging for Targets with Complex Motion under Low SNR Condition.

This paper proposes a novel bistatic inverse synthetic aperture radar (ISAR) imaging algorithm for the target with complex motion under low signal to noise ratio (SNR) condition. Note the bistatic ISAR system generally suffers from a lower SNR than the monostatic one because of its non-mirror reflection geometry. A de-noising method, therefore, is proposed to improve SNR of range profiles, which accumulates the aligned range profiles non-coherently to obtain a window for noise suppression. Additionally, since the complex motion of target induces nonstationary Doppler, which is destructive to ISAR imaging, an optimal coherent processing interval (CPI) selection algorithm is further proposed to find out the interval where the Doppler is relatively stationary, so as to produce well-focused ISAR images. It utilizes the reassigned time-frequency (TF) method to obtain the high resolution instantaneous Doppler spectrum, and the minimum entropy criterion to select the optimal CPI, respectively. Note the selected CPI often contains too limited pulses to produce ISAR images with high resolution. A sparse aperture ISAR imaging method within the Bayesian framework is further proposed, which introduces the Laplacian scale mixture (LSM) model as the sparse prior, so as to reconstruct well-focused ISAR images with high resolution and low side lobes from the limited data. Compared with the traditional sparse Bayesian learning method, the proposed LSM based ISAR imaging performs superiorly on resolution improvement and noise reduction. Experimental results based on both simulated and measured data validate the effectiveness of the proposed algorithms.

The Improved Adaptive Silence Period Algorithm over Time-Variant Channels in the Cognitive Radio System

In the field of cognitive radio spectrum sensing, the adaptive silence period management mechanism (ASPM) has improved the problem of the low time-resource utilization rate of the traditional silence period management mechanism (TSPM). However, in the case of the low signal-to-noise ratio (SNR), the ASPM algorithm will increase the probability of missed detection for the primary user (PU). Focusing on this problem, this paper proposes an improved adaptive silence period management (IA-SPM) algorithm which can adaptively adjust the sensing parameters of the current period in combination with the feedback information from the data communication with the sensing results of the previous period. The feedback information in the channel is achieved with frequency resources rather than time resources in order to adapt to the parameter change in the time-varying channel. The Monte Carlo simulation results show that the detection probability of the IA-SPM is 10–15% higher than that of the ASPM under low SNR conditions.

A Robust and Higher Precision Time Delay Estimation Method Facing Low Signal to Noise Ratio Conditions

Many available signals in the real world are usually weak with impulse noises and/or outliers, and we also need to have higher estimation precision in applications. Our focus of attention is pretty much on integrating robustness and accuracy under lower signal to noise ratio (SNR) with impulse noises. Although traditional fractional adaptive time delay estimation (TDE) methods have higher precision, the results of estimation are unreasonable when the signals contain some impulse noises. While, most proposed robust algorithms later can work well mainly with high SNR. In this paper, considering the practical problem in equipment fault acoustic localization based on TDE methods, an improved robust fractional adaptive time delay estimation method is addressed facing lower SNR conditions. First, the impulse noises are modeled as Alpha stable distribution, and the integer part of TDE is getting by using covariate correlation approach. Then, the integer estimation value is used as initial parameter value of time delay. Covariant sequence is the input of time delay estimator. Next, fractional TDE value is adaptive obtained by iteration under minimum average <i>p</i> norm criterion. Covariant sequence weakens irrelevant noises, meanwhile preserves time delay information between original sequences. Computer simulations and comparative experiments show that improved method has better estimation results. This method is robust and higher precision, and especially under impulse environment and low SNR conditions.

A High-Precision Method of Phase-Derived Velocity Measurement and Its Application in Motion Compensation of ISAR Imaging

The existing methods for motion compensation in inverse synthetic aperture radar (ISAR) imaging are generally limited to the low-order target motion model, and require iterative optimization with limited velocity estimate precision and heavy computational burdens. This paper proposes a high-precision method of phase-derived velocity measurement (PDVM) and applies it to motion compensation of ISAR imaging. The method applies PDVM based on range profiles cross correlation to the translational velocity estimation of targets, and converts the velocity measurement results to the corresponding range increment. The equivalent phase-derived range measurement precision can reach the order of magnitude of millimeter (mm) or even sub-mm, which can satisfy the precision requirements of both envelope alignment and phase adjustment. The key to realizing PDVM is resolving phase ambiguity. The traditional method for resolving ambiguity has very high requirements for the signal-to-noise ratio (SNR). This work resolves ambiguity by combining multiframe data, i.e., by resolving ambiguity of multiframe data simultaneously instead of resolving ambiguity of single-frame data independently and correcting the above ambiguity-resolving results using a minimum-entropy method. Therefore, phase ambiguity can be correctly resolved under a relatively low SNR. Experimental results of an ISAR imaging of an airplane show that the method proposed in this paper can obtain high-quality ISAR imagery, and can efficiently realize robust imaging under the conditions of low SNR.

Impact of light-adaptive mechanisms on mammalian retinal visual encoding at high light levels.

A persistent change in illumination causes light-adaptive changes in retinal neurons. Light adaptation improves visual encoding by preventing saturation and by adjusting spatiotemporal integration to increase the signal-to-noise ratio (SNR) and utilize signaling bandwidth efficiently. In dim light, the visual input contains a greater relative amount of quantal noise, and vertebrate receptive fields are extended in space and time to increase SNR. Whereas in bright light, SNR of the visual input is high, the rate of synaptic vesicle release from the photoreceptors is low so that quantal noise in synaptic output may limit SNR postsynaptically. Whether and how reduced synaptic SNR impacts spatiotemporal integration in postsynaptic neurons remains unclear. To address this, we measured spatiotemporal integration in retinal horizontal cells and ganglion cells in the guinea pig retina across a broad illumination range, from low to high photopic levels. In both cell types, the extent of spatial and temporal integration changed according to an inverted U-shaped function consistent with adaptation to low SNR at both low and high light levels. We show how a simple mechanistic model with interacting, opponent filters can generate the observed changes in ganglion cell spatiotemporal receptive fields across light-adaptive states and postulate that retinal neurons postsynaptic to the cones in bright light adopt low-pass spatiotemporal response characteristics to improve visual encoding under conditions of low synaptic SNR.