Low Signal-to-noise Ratio Conditions Research Articles

Waveform centroid discrimination of pulsed Lidar by combining EMD and intensity weighted method under low SNR conditions

In this study, we report on a self-adaptive waveform centroid algorithm combing EMD and intensity-weighted (IW) method for the accurate Lidar ranging under low signal-to-noise ratio (SNR) conditions. Empirical mode decomposition (EMD), discrete cosine transformation (DCT) and inverse discrete cosine transformation (IDCT) are utilized to filter out the high-frequency interference in Lidar pulse signal. A time window is set to adaptive select the effective data. The centroid points are calculated by the intensity-weighted (IW) waveform centroid algorithm with the selected data. The proposed algorithm is experimentally tested, achieving an average error of about 153 ps, 209 ps and 232 ps under the SNR of 5.1 dB, 2.7 dB and 1.8 dB, respectively, which exerts better precision compared to the wavelet soft thresholding with intensity-weighted (WL-IW) algorithm and EMD with peak selection (EMD-PK) algorithm. Furthermore, the proposed algorithm is fairly robust with remarkable detection success rates of above 96% under low SNR conditions. The proposed waveform centroid algorithm has the potential for pulsed Lidar’s ranging tasks in harsh environment.

Intra‐pulse modulation radar signal recognition based on CLDN network

Automatic modulation classification of radar signals, which plays a significant role in both civilian and military applications, is researched in this study through a deep learning network. In this study, a novel network combined a shallow convolution neural network (CNN), long short-term memory (LSTM) network and deep neural network (DNN) is proposed to recognise six types of radar signals with different signal-to-noise ratio (SNR) levels from -14 to 20 dB. First, raw signal sequences in the time domain, frequency domain and autocorrelation domain are as input for a shallow CNN. Then the features extracted by CNN will be the input of LSTM network. Finally, DNNs will output the signal modulation types directly. The simulation results demonstrate that the accuracies in autocorrelation domain are all more than 90% at -6 dB and close to 100% when SNR > -2 dB. The recognition performances of the three domains are compared. Compared with other recognition methods, the proposed method has higher average accuracy and better performance under low SNR condition. The measured results show that the proposed method has achieved high accuracies of common four kinds of measured radar signals.

Robust calibration method for distributed ISAR time‐varying frequency errors based on the contrast maximisation principle

The linear time-varying frequency errors (LTFE) caused by the mismatch of transmitter and receiver oscillators can defocus the imaging result of distributed inverse synthetic aperture radar (ISAR) seriously. The LTFE calibration method based on the entropy minimization principle is sensitive to signal-to-noise ratio (SNR), and its performance is degraded significantly under low SNR conditions. In addition, this method uses enumeration algorithm to solve the optimization problem, which has a heavy computation burden. Therefore, a robust calibration method based on the contrast maximization principle is proposed. Compared with image entropy, image contrast has better anti-noise ability because it has better sensitivity property, namely, the change of image contrast is sharper than the change of image entropy. In the proposed method, the estimation of frequency error coefficient is modelled as an unconstrained optimization problem with image contrast as cost function, and the particle swarm optimization (PSO) algorithm is used to search the global optimal solution. Then, the LTFE can be calibrated by the estimated frequency error coefficient. The proposed method has better robustness, which can work well under low SNR conditions. Besides, it has higher computational efficiency. Simulations are carried out to verify the effectiveness and robustness of the proposed method.

Source number estimation based on a novel multi-view meta-hierarchical classification framework

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 normally hard to determine using the traditional Akaike information criterion or minimum description length methods, especially in low or very low signal-to-noise ratio (SNR) conditions. In this paper, we propose to estimate the source number in a data-driven manner by employing a novel multi-view meta-hierarchical classification framework. Specifically, there are two collaborative views and two hierarchical classification layers employed for generating meta-features. Then, the obtained meta-features are re-learned by the final meta-classification layer to estimate the final prediction of the source number. Experimental results illustrated that the proposed method can estimate the source number accurately and reliably even in low SNR conditions.

A TV Forward-Looking Super-Resolution Imaging Method Based on TSVD Strategy for Scanning Radar

Because of the poor performance of the conventional total variation (TV) super-resolution imaging method in low signal-to-noise ratio (SNR) condition, a TV super-resolution imaging method based on the truncated singular value decomposition (TSVD) strategy is proposed. First, based on the regularization theory, the TV function is selected as the constraint term to construct objective function. Second, to solve the problem of noise amplification faced by the conventional TV method, this article reconstructs the objective function based on the TSVD strategy, which improves the antinoise performance by discarding small singular values of antenna convolution matrix. Finally, due to the nondifferentiable property of reconstructed objective function, this article utilizes the iterative reweighted norm (IRN) method. Since the influence of the noise is weakened by the TSVD strategy, the proposed method can achieve super-resolution imaging and contour preservation in low SNR condition. The simulation and experimental results demonstrate the effectiveness of the proposed method.

Enhanced Loran skywave delay estimation based on artificial neural network in low SNR environment

This study proposes a high precision algorithm to estimate the Enhanced Loran skywave delay. It is based on the artificial neural network. The algorithm establishes a neural network model between the receiving Enhanced Loran signal and the skywave propagation delay. By training a large number of data, the neural network model can more accurately reflect the relationship between the receiving signal and the skywave delay. This is an innovative application of the skywave delay estimation algorithm in the Enhanced Loran receiving system, especially in low signal-to-noise ratio (SNR) environments. The experimental results show that the accuracy of this algorithm is about hundreds of nanoseconds in the condition of normal receiving SNR. The accuracy of the algorithm is about us in the condition of low SNR, while the previous algorithm cannot be used in this case. It has also been proved by the off-air data.

Accurate DOA Estimation With Adjacent Angle Power Difference for Indoor Localization

Indoor localization with high accuracy plays a key role in the field of Internet of Things in 5th-Generation (5G) era. With the introduction of Multiple Input Multiple Output (MIMO) technology in 5G, the direction-of-arrival (DOA) method is highly feasible in indoor localization. However, the direction of arrival is susceptible to complex indoor environment. To improve the accuracy and stability of DOA estimation, an adjacent angle power difference (AAPD) method is proposed based on Orthogonal Matching Pursuit (OMP). This method uses OMP to obtain an initial estimate of the direction, and then, adjusts the estimation by calculating the difference power of adjacent points at initial value point to get the fractional DOA. In the scenario of continuous movement, beamforming is further applied, which reduces the amount of calculation. Both simulation and experimental results show that the proposed method can achieve high accuracy and eliminate the error jitter. Compared with the classical Multiple Signal Classification (MUSIC) method for DOA estimation, the proposed method can increase accuracy by 46% under the condition of low SNR (Signal-to-Noise Ratio). The probability that the measurement error does not exceed 5° in the actual movement tests is 97.5%.

Automatic Modulation Recognition for Radar Signals via Multi-Branch ACSE Networks

Automatic modulation recognition (AMR) for radar signals plays a significant role in electronic warfare. Conventional recognition methods may suffer from the recognition accuracy and the computation complexity under low signal-to-noise ratio (SNR) conditions. In this paper, a novel multi-branch Asymmetric Convolution Squeeze-and-Excitation (ACSE) networks using multi-domain features and fusion strategy based on a support vector machine is proposed to recognize eight kinds of radar signals. First, features of radar signals in the frequency domain, the autocorrelation domain, and the time-frequency domain are extracted. Then the obtained multi-domain features are converted as the input of the proposed networks which owns the representational power and learning ability. Finally, the outputs of multi-branch ACSE networks are fused via the fusion strategy to obtain the final results. Via simulations, the robustness and effectiveness of the fusion strategy are verified. The results on the simulation dataset prove that the proposed method can achieve more than 93% accuracy at -10dB for all modulations. Compared with four newly proposed networks, the multi-branch ACSE networks achieves better performance under low SNR conditions. And the results on measured signals show that the proposed method outperforms other comparison methods, especially for binary frequency-shift keying (BFSK) signals.

DOA estimation of wideband sources by sparse recovery based on uniform circle array

The conventional direction of arrival (DOA) estimation by compressed sensing is based on wideband focusing and sparse recovery in discrete domain. The former needs high signal to noise ratio (SNR), and the latter requires the space division, or their performance will degrade seriously. Therefore, a new algorithm of wideband DOA calculation in frequency domain based on uniform circle array (UCA) is proposed, the optimisation problems are respectively disposed at each frequency, then the sparse support set is acquired by the corresponding semidefinite programming, after that DOA is calculated through fusing the data of every frequency, simultaneously the original sources can also be reconstructed. This algorithm averts the grid partition in discrete domain, and it also performs well in condition of small samples and low SNR.

A High-Precision Phase-Derived Velocity Measurement Method for High-Speed Targets Based on Wideband Direct Sampling LFM Radar

This paper proposes a phase-derived velocity measurement (PDVM) method for high-speed targets based on wideband direct sampling linear frequency modulated radar. First, a high-speed target echo model considering intrapulse Doppler modulation is developed. Then, a PDVM model considering acceleration is established. The key to realizing PDVM is resolving phase ambiguity. Under low signal-to-noise ratio (SNR) conditions, a joint processing method combining acceleration information and multiframe data to solve phase ambiguity is proposed, which can significantly reduce the SNR requirement for PDVM. In this paper, the small-amplitude micromotion measurement capability of the proposed method is verified by simulation. Moreover, the measured data of a Ku-band ground-based radar are used to verify the applicability of the PDVM method under low SNR conditions and its feasibility to be applied to complex multi-scattering point targets. Both the simulation and experimental results show that the proposed method is suitable for high-speed targets with radial motion, including acceleration and jerk, and that the PDVM precision can reach the order of magnitude of centimeters per second or millimeters per second.

Low-complexity TDOA and FDOA localization: A compromise between two-step and DPD methods

Conventional two-step passive localization methods have poor localization precision when the input signal-to-noise ratio (SNR) is low, although they are usually computationally attractive. The direct position determination (DPD) methods, on the contrary, exhibit stronger robustness to low SNRs at the cost of much higher computational complexity. In this paper, an expectation-maximization (EM)-based computationally inexpensive localization method is proposed, which serves as a compromise between the two-step methods and DPD methods. During each iteration of the proposed method, the constraint that all intermediate parameters correspond to a common source position is taken into account explicitly, and this constraint is used to refine the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements. Then the source position is updated using these renewed measurements. The proposed method has superior localization performance under low SNR conditions compared to the two-step methods at the cost of limited increment in computational load, and its computational complexity is much lower than DPD methods at the cost of acceptable accuracy degradation. Moreover, the proposed method is applicable to other localization scenarios as well with appropriate modifications. Simulation results demonstrate the effectiveness of the proposed method.

Development of Weak Signal Recognition and an Extraction Algorithm for Raman Imaging.

Improving time resolution of Raman imaging is essential for the observation of dynamic processes involved in interfacial catalysis and biological systems. The crucial step is how to recognize and extract weak Raman signals overwhelmed in the strong noise under the low signal-to-noise ratio (SNR) condition. Here, by exploring the relationship between the SNR of a single Raman spectrum and the structural similarity (SSIM; the key parameter evaluating the image quality) of the whole image, we determined a semiempirical threshold with SNR = 0 dB for clear imaging for the first time. Therefore, we proposed one signal processing algorithm for fast Raman imaging by reconstructing the Raman spectrum with the aid of weak signal processing: extracting the reliable Raman signal of the target under the low SNR and then determining the suitable scanning time to obtain the Raman image with a trustworthy image quality. In the first step, fast Fourier transform (FFT), least squares, and 2-D median filter are sequentially applied to improve the SNR of each raw Raman spectrum. In the second step, a local SNR evaluation strategy is developed to predict image quality as well as the determination of clear imaging. The proposed method was successfully applied to the fast imaging of the cell under the low SNR condition.

A fast decoupled ISAR high-resolution imaging method using structural sparse information under low SNR

Inverse synthetic aperture radar (ISAR) image can be represented and reconstructed by sparse recovery (SR) approaches. However, the existing SR algorithms, which are used for ISAR imaging, have suffered from high computational cost and poor imaging quality under a low signal to noise ratio (SNR) condition. This paper proposes a fast decoupled ISAR imaging method by exploiting the inherent structural sparse information of the targets. Firstly, the ISAR imaging problem is decoupled into two sub-problems. One is range direction imaging and the other is azimuth direction focusing. Secondly, an efficient two-stage SR method is proposed to obtain higher resolution range profiles by using jointly sparse information. Finally, the residual linear Bregman iteration via fast Fourier transforms (RLBI-FFT) is proposed to perform the azimuth focusing on low SNR efficiently. Theoretical analysis and simulation results show that the proposed method has better performance to efficiently implement higher-resolution ISAR imaging under the low SNR condition.

Flame front identification and its effect on turbulent premixed flames topology at high pressure

Image processing is of primary importance in the laser diagnostic on turbulent combustion, which can provide quantitative information, such as velocity field, intermediate species profile. Turbulent premixed flame front indicates the flame-turbulence interaction, can be decided from the OH-PLIF (Planar Laser Induced Fluorescence) technique due to the sharp increase of the OH distribution from unburned to burned region. In this paper, an adaptive threshold binarization method was proposed based on the local gray histogram of the OH-PLIF images when the signal-noise ratio is relatively low, i.e. turbulent flames at intensive turbulence and high pressure. The noise was eliminated by seed-mediated growth to obtain the legible flame front. Effect of different flame front identification methods, namely global binarization method, the canny operator of Matlab and the proposed adaptive threshold binarization method, on the flame structure parameters at various conditions was investigated. Results show that the flame fronts are seriously vague using the global binarization and the canny operator when the signal-noise ratio is low at intensive turbulence and high pressure, while continuous flame front can still be attracted by the proposed adaptive threshold binarization method. The turbulent flame front parameters from different methods are almost identical at weak turbulence and atmospheric pressure. While due to the noise introduced, the value of flame brush thickness, flame height and flame surface density from the proposed method is smaller than that of other two methods at high pressure. The adaptive threshold binarization method can obtain a better flame front identification at low signal-noise ratio conditions, results in reasonable premixed turbulent flame front parameters at intensive turbulence and high pressure.

Shrinkage sinusoidal phase estimation based on spherical simplex unscented transform and bootstrap method

AbstractThis paper presents novel shrinkage‐based sinusoidal phase estimation algorithms. The main contributions of this paper are two‐fold. First, the shrinkage factor is found using the spherical simplex unscented transform (SSUT) and the combination of bootstrap and SSUT to reduce the computational complexity of the Monte Carlo method. The computational burden of the proposed methods is relatively low due to the use of SSUT, resulting from the carefully selected sigma points. Second, the accuracy of the novel shrinkage estimator is better than that of the maximum likelihood estimator (MLE) and existing shrinkage estimator under the low signal‐to‐noise ratio (SNR) and small sample number conditions. It is demonstrated that the resulting accuracies of the proposed shrinkage‐based phase estimation methods are superior to those of existing shrinkage algorithm and MLE in low SNR and small sample number conditions.

Investigation of word recognition for the elderly in speech and noise spatial separation

In present study, subjective Chinese Mandarin word recognitions were carried out by 10 elderly listeners aged more than 60 years old in three rooms of a nursing house under the different reverberation time (RT), different signal-to-noise ratio (SNR) and different azimuth of noise conditions. The results showed that word recognition scores of the elderly increased with the increase of SNR and the spatial separation azimuth between speech and noise, and decreased with the increase of RT. The improvements of word recognition scores for spatial separation azimuth between speech and noise from 0° to 45° were more than those from 45° to 90° in all three rooms for the elderly. There was a great difference in the word recognition scores of hearing impairment elderly and normal hearing elderly under low SNR conditions and the difference decreased with the increase of SNR. The difference in the word recognition scores between hearing impairment participants and normal hearing participants also became smaller when the target speech and noise was spatially separated.

Imaging DNA Equilibrated onto Mica in Liquid Using Biochemically Relevant Deposition Conditions.

For over 25 years, imaging of DNA by atomic force microscopy has been intensely pursued. Ideally, such images are then used to probe the physical properties of DNA and characterize protein-DNA interactions. The atomic flatness of mica makes it the preferred substrate for high signal-to-noise ratio (SNR) imaging, but the negative charge of mica and DNA hinders deposition. Traditional methods for imaging DNA and protein-DNA complexes in liquid have drawbacks: DNA conformations with an anomalous persistence length ( p), low SNR, and/or ionic deposition conditions detrimental to preserving protein-DNA interactions. Here, we developed a process to bind DNA to mica in a buffer containing both MgCl2 and KCl that resulted in high SNR images of equilibrated DNA in liquid. Achieving an equilibrated 2D configuration ( i. e., p = 50 nm) not only implied a minimally perturbative binding process but also improved data quality and quantity because the DNA's configuration was more extended. In comparison to a purely NiCl2-based protocol, we showed that an 8-fold larger fraction (90%) of 680-nm-long DNA molecules could be quantified. High-resolution images of select equilibrated molecules revealed the right-handed structure of DNA with a helical pitch of 3.5 nm. Deposition and imaging of DNA was achieved over a wide range of monovalent and divalent ionic conditions, including a buffer containing 50 mM KCl and 3 mM MgCl2. Finally, we imaged two protein-DNA complexes using this protocol: a restriction enzyme bound to DNA and a small three-nucleosome array. We expect such deposition of protein-DNA complexes at biochemically relevant ionic conditions will facilitate biophysical insights derived from imaging diverse protein-DNA complexes.

A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine

Rolling-element bearings (REBs) faults are one of the most common breakdowns of rotating machines, thus proposing effective bearing fault diagnosis and classification methods is vital. In previous studies, lots of bearing fault classification methods have been proposed to solve the problem in low signal-to-noise ratio (SNR) conditions. Though satisfactory classification results have been obtained, in consideration of the practicability and application scenarios, there are still many aspects to improve, such as the complexity of method and the classification ability in lower SNR conditions. Therefore, this paper presents a novel method that combines lower-order moment spectrum with support vector machine (SVM) for bearing fault classification in low SNR conditions. The lower-order moment spectrum reduces influence of Gaussian noise and enhances the quality of fault feature. A bandpass filter group (BPFG) has been used to reduce the dimension of the lower-order moment spectra (LOMS) as feature vectors. And a following SVM has been applied as the fault classifier, due to the mature application and satisfactory performance in fault classification. The proposed method is demonstrated to have strong ability of classification in low SNR conditions experimentally.

A protocol for structured illumination microscopy with minimal reconstruction artifacts

The imaging rate of structured illumination microscopy (SIM) reached 188 Hz recently. As the exposure time decreases, the camera detects fewer virtual photons, while the noise level remains the same. As a result, the signal-to-noise ratio (SNR) decreases sharply. Furthermore, the SNR decreases further because of photobleaching and phototoxicity. This decreased quality of SIM raw data may lead to surprising artifacts with various causes, which may confuse a new user of SIM microscopy. We summarize three significant possible sources of severe artifacts in reconstructed super-resolution (SR) images. Ultrafast motion of a biological sample or an uneven illumination pattern is the most difficult to be identified. The estimated parameter could also be incorrect, leading to artifact of regular patterns. Furthermore, reconstruction with the Wiener method generates stochastic artifacts due to the amplification of noise during the deconvolution process. To deal with these problems, we have established a protocol to reconstruct ultrafast SIM raw data obtained in low SNR conditions. First, we checked the quality of the raw data with the ImageJ plugin SIMcheck before reconstruction. Then, a modified parameter estimation method was used to improve the precision of the parameters. Finally, an iterative algorithm was used for SIM reconstruction under low signal-to-noise ratio conditions. This procedure effectively suppressed the artifacts in the super-resolution images reconstructed from raw data of low signal-to-noise ratio.

High-Precision Light Spot Position Detection in Low SNR Condition Based on Quadrant Detector

In free space optical communications, long-distance transmission leads to the attenuation of beacon light, where we adopt a quadrant detector (QD) to receive the weak signal. However, the background light interferes so strongly that the output signal-to-noise ratio (SNR) of QD is at a low level, which causes a decrease in accuracy of the direct detection method. This requires finding a new light spot detection method, so an improved detection method is proposed. Because the dark current noise and the background light noise are both white noise, we adopt a Kalman filter to estimate the real output of four electric signals of QD. Unfortunately, running these through an amplifier introduces some direct current (DC) offsets into the signals. In order to balance the effect of the DC offsets, we consider using the modulation method, where we employ a sine signal to modulate the intensity of the beacon light at the transmitting end, after which we can give an inverse gain to move the center of signals to near zero to eliminate the DC offsets when we calculate the data. In Kalman filtering, we use the peak values of the signals in every period after the analog to digital converter (ADC) as the elements of the measurement matrix. Experimental results show that even when QD output SNR is about −10 dB, the detection root-mean-square errors decrease by 51.5% using the improved detection method compared with the direct detection method. Moreover, Kalman filtering does not require a large amount of data, which means it works efficiently, can reduce the cost of hardware resources, and is available for the real-time calculation of spot position.