Signal-to-noise Ratio Conditions Research Articles

Automatic Modulation Recognition of Dual-Component Radar Signals Using ResSwinT-SwinT Network

Automatic modulation recognition plays an important role in military and civilian applications, identifying the modulation format of received signals before signal demodulation. With the increasing complexity and density of the electromagnetic environment, the multi-component modulation radar signal recognition against various signal-to-noise ratio (SNR) conditions has become a practical and urgent problem. In this paper, we propose a dual-component modulation recognition framework, which incorporates the residual Swin transformer denoise network (ResSwinT), Swin transformer feature extraction network (SwinT), residual-attention (RA) modulation recognition head, and SNR level classifier and achieves robust recognition performance against various SNR conditions with tolerable complexity and accuracy trade-off. Firstly, the time-frequency analysis is employed to transform dual-component radar signals into time-frequency images (TFIs). Then, the TFIs at various SNR levels are applied to the SwinT, which generates shallow and deep feature representations for the SNR classifier and RA-modulation recognition head, respectively. The ResSwinT is initiated to reconstruct low SNR TFIs only, which are again processed by the SwinT. Finally, the RA-modulation recognition head provides modulation format predictions. The proposed framework can identify randomly combined dual-component radar signals from 12 modulation formats, meanwhile, improving the utilization of the SwinT feature and reducing unnecessary computation of the ResSwinT. Simulation results show that the proposed scheme can obtain an exact match ratio (EMR) of larger than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {97\%}$</tex-math></inline-formula> at SNR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {&gt;\!-6}$</tex-math></inline-formula> dB. At low SNR condition ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {-12}$</tex-math></inline-formula> dB), the ResSwinT can obtain about EMR gain of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {20\%}$</tex-math></inline-formula> and the overall framework can achieve EMR of more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {80\%}$</tex-math></inline-formula> , which outperforms other state-of-the-art methods and obtains better generalization capability.

Toward Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification

Radio frequency fingerprint identification (RFFI) can classify wireless devices by analyzing the signal distortions caused by intrinsic hardware impairments. Recently, state-of-the-art neural networks have been adopted for RFFI. However, many neural networks, e.g., multilayer perceptron (MLP) and convolutional neural network (CNN), require fixed-size input data. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the RFFI performance in such scenarios is often unsatisfactory. In this paper, we analyze the reason why MLP- and CNN-based RFFI systems are constrained by the input size. To overcome this, we propose four neural networks that can process signals of variable lengths, namely flatten-free CNN, long short-term memory (LSTM) network, gated recurrent unit (GRU) network, and transformer. We adopt data augmentation during training which can significantly improve the model’s robustness to noise. We compare two augmentation schemes, namely offline and online augmentation. The results show the online one performs better. During the inference, a multi-packet inference approach is further leveraged to improve the classification accuracy in low SNR scenarios. We take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low-SNR classification accuracy by up to 50% and the multi-packet inference approach can further increase the accuracy by over 20%.

E2DTF: An End-to-End Detection and Tracking Framework for Multiple Micro-UAVs With FMCW-MIMO Radar

Due to the weak radar echoes and strong background clutters in low-altitude airspace, the detection and tracking for multiple micro-unmanned aerial vehicles (UAVs) have posed formidable challenges in radar surveillance field. Consequently, this paper proposes an end-to-end detection and tracking framework (E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTF) for multiple micro-UAVs by utilizing the frequency modulated continuous wave-multiple input multiple output (FMCW-MIMO) radar. To address the low signal-to-noise ratio (SNR) problem, E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTF presents a frame-range-Doppler-azimuth information fusion filter to integrate the target energy by exploiting the spatio-temporal dependence of positions within a sequence of unthresholded frames. Additionally, considering that a target may enter/leave the radar field-of-view (FOV), E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTF introduces a target model state, updated by an extended Markov state transition matrix sequentially, to realize an unknown, time-varying number of micro-UAVs tracking. Another nice feature of E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTF is that it avoids the complex data association procedure thanks to removing the threshold-decision operation. Finally, both numerical simulations and experiments with five different scenarios, i.e., horizontal line, cross-trajectory, circular loop, rainy condition and 3D trajectory tracking are presented to verify the effectiveness of the proposed method. The results show that E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTF can obtain superior detection and tracking performance for multiple micro-UAVs in contrast to the state-of-the-art methods considering detection and tracking processes independently, especially under low SNR conditions.

An Intelligent Particle Filter for Infrared Dim Small Target Detection and Tracking

With consideration of low tracking accuracy and even losing target when the track-before-detect method based on particle filter (PF-TBD) tracks infrared dim small target in the complex background. In this article, a track-before-detect method based on the spring model firefly algorithm optimization particle filter (SFA-PF-TBD) was proposed to address this problem. First, the attractiveness and movement behavior of fireflies were introduced into the particle filter for optimizing the particles, and the optimization strength was controlled by evaluating the real-time distribution of particles. After optimization, detecting the density of the particles around the optimal particle, the elastic mechanism of spring was used to control the density of particles around the optimal particle when the particles gathered excessively, which made the distribution of particles more reasonable. Then, the TBD method was realized by the improved particle filter for tracking dim small target under the low signal-noise ratio conditions. Finally, we compared the SFA-PF-TBD algorithm with other algorithms by tracking experiments in simulation scenes and actual scenes. The results showed that the SFA-PF-TBD algorithm has more advantages than the PF-TBD algorithm.

Deep Unfolding Contrast Source Inversion for Strong Scatterers via Generative Adversarial Mechanism

To alleviate the extremely intrinsical ill-posedness and nonlinearity of electromagnetic inverse scattering under high contrast and low signal to noise ratio (SNR), we propose a deep unfolding network based on generative adversarial network (GAN) under contrast source inversion (CSI) framework, termed UCSI-GAN. The method solves inverse scattering problems (ISPs) using end-to-end generating confrontation way by incorporating a physical model together with its iterative updating formulation into the internal architecture of GAN. First, the nonlinear iterative scheme is extended to a deep unfolding generator network, and the contrast source and contrast updates are mapped to each module of the generator network. Second, to stabilize the imaging process, we add a refinement network to each variable update. Finally, the discriminator network is employed to ensure the authenticity of reconstructed images. The generator network and discriminator network are alternately trained with a generative adversarial learning strategy to reconstruct the properties of the medium object. Numerical experiments demonstrated that the performance of UCSI-GAN is better than traditional CSI and state-of-the-art learning approaches under high contrast and low SNR condition.

Adaptive channel decoding method for polar codes

The polar code is a channel coding technology which can reach the Shannon limit in theory and has the advantage of low coding and decoding complexity. It is now one of the channel coding solutions in 5G communication. The successive cancellation list (SCL) decoding algorithm is the most commonly used decoding method for polar codes, but it has high memory and time complexity. Fast succesive cancellation (Fast-SC) decoding algorithm can effectively reduce the decoding complexity, but it has the problem of low reliability. In order to take into account the decoding efficiency and reliability of polar codes, a preFast-SCL decoding algorithm for adaptive channels is proposed. The algorithm combines the advantages of Fast-SC and SCL decoding algorithms. At the beginning of decoding, the Fast-SC algorithm is used to quickly obtain a group of decoding results and verify them. If the verification is passed, it will be outputed as the result, but if not, SCL decoding will be used to ensure the reliability. Simulation results show that the complexity of preFast-SCL decoding algorithm decreases gradually with the improvement of channel conditions. In additive white Gaussian noise channel, the reliability of preFast-SCL is basically the same as that of SCL decoding algorithm. When the signal to noise ratio (SNR) is 2.0 dB, the decoding complexity of preFast-SCL is reduced by 45% compared with SCL, and the time performance gain is better under the condition of higher SNR.

Proportional Selection Scheme: A Frequency Band Division Tool for Rolling Element Bearing Diagnostics

The cyclic spectral coherence analysis has been proved to be a useful tool to reveal the hidden periodic or repetitive patterns, and how to determine an optimal demodulation band is crucial for characterizing the fault characteristic of the rolling element bearing (REB). Thus, a proportional selection scheme based on a bottom-up strategy is proposed in this study, which can determine an optimal demodulation band and realize bearing fault feature extraction under the conditions of poor frequency resolution and a lower signal-to-noise ratio (SNR). First, compared with conventional frequency division structures, such as the 1/3-binary tree structure, the finer frequency band groups with the same bandwidth in each division level can be obtained automatically and strictly under poor frequency resolution conditions. Second, the richness of the REB fault information of each divided frequency band can be evaluated by the improved diagnostic feature indicator under a lower SNR condition. By using the scheme, the fault characteristic of the REB can be exposed. The efficacy of the proposed scheme is supported by simulations and experiments under some tough conditions

Minimal number of sampling directions for robust measures of the spherical mean diffusion weighted signal: Effects of sampling directions, b-value, signal-to-noise ratio, hardware, and fitting strategy

Several recent multi-compartment diffusion MRI investigations and modeling strategies have utilized the orientationally-averaged, or spherical mean, diffusion-weighted signal to study tissue microstructure of the central nervous system. Most experimental designs sample a large number of diffusion weighted directions in order to calculate the spherical mean signal, however, sampling a subset of these directions may increase scanning efficiency and enable either a decrease in scan time or the ability to sample more diffusion weightings. Here, we aim to determine the minimum number of gradient directions needed for a robust measurement of the spherical mean signal. We used computer simulations to characterize the variation of the measured spherical mean signal as a function of the number of gradient directions, while also investigating the effects of diffusion weighting (b-value), signal-to-noise ratio (SNR), available hardware, and spherical mean fitting strategy. We then utilize empirically acquired data in the brain and spinal cord to validate simulations, showing experimental results are in good agreement with simulations. We summarize these results by providing an intuitive lookup table to facilitate the determination of the minimal number of sampling directions needed for robust spherical mean measurements, and give recommendations based on SNR and experimental conditions.

DOA estimation algorithm based on spread spectrum sequence in low signal-to-noise ratio

Spread spectrum communication is a common communication method in underwater communication. Based on the space-time processor received by the array, it can filter the signals arriving along each path separately. Combined with the diversity of space-time clusters, it can effectively improve the communication system’s reliability. The core problem of the space-time processor is the direction of arrival (DOA) and signal source number estimation. Based on the good self-coherence of the spread spectrum sequence, this paper proposes a multiple signal classification algorithm (MUSIC) for accurate DOA estimation. However, since the MUSIC algorithm uses the received signal’s covariance matrix for DOA estimation, the number of sources needs to be predicted in advance. Under a low signal-to-noise ratio (SNR), the signal eigenvalues and the noise eigenvalues of the covariance matrix differ slightly, which makes signal source number estimation difficult. To address this issue, a singular value decomposition method using the delay structure information of the array element is proposed to estimate the number of sources of the spreading sequence under a low SNR. The method proposed in this paper can well estimate the DOA of the signal under a low SNR. Meanwhile, there is no need to convert the signal to the individual sub-bands, which effectively reduces the calculation overhead. At the same time, the Hankel matrix is used to solve the problem that the MUSIC algorithm cannot accurately estimate the number of signal sources under the condition of low SNR. Compared with the conventional algorithm, the Hankel matrix can more accurately estimate the number of signal sources in the case of low SNR. Through simulation experiments, the effectiveness of our DOA estimation algorithm is validated under a low SNR.

A Two-Stage High-Order Modulation Recognition Based on Projected Accumulated Constellation Vector in Non-Cooperative B5G OSTBC-OFDM Systems

Blind modulation recognition (BMR) is a pivotal signal processing technology for cognitive receivers to reduce signaling overhead and improve transmission efficiency. Although BMR techniques have been extensively explored for single-carrier systems, only a few research have been published for OSTBC-OFDM systems. More troubling is that high-order modulation types will make the BMR harder because the constellation points are overcrowded. To remedy these flaws, we propose a two-stage high-order BMR approach that leverages low-complexity cumulants and projected accumulated constellation vector (P-ACV) to recognize high-order modulation types (e.g., 2048QAM). Firstly, zero-forcing blind equalization is employed to recover the damaged signal and enhance its feature representation. Next, the first-stage recognition module leverages the fourth-order cumulants to classify 13 modulation types into three groups, including PSK&PAM, square-shaped QAM and cross-shaped QAM groups. Finally, the second-stage recognition module distinguishes the inter-class signal in each group by P-ACV features. The temporal convolution network is built to tap the potentialities of P-ACV and speed up online inference. Numerical results demonstrate that our two-stage algorithm obtains a recognition accuracy of 97.37% when signal-to-noise-ratio (SNR) ≥ 12 dB and performs six times faster than the suboptimal method. Notably, it does not require prior knowledge, such as channel state information (CSI) and SNR conditions.

A photon-number-based systematic algorithm for range image recovery of GM-APD lidar under few-frames detection

The GM-APD lidar can detect the single-photon level echo signal and obtain three-dimensional range information, but the imaging results are greatly affected by noise and statistical frame number. Considering these problems, this paper proposes a photon-number systematic (PNS) algorithm under a few statistical frames in low SNR (signal-to-noise ratio). It includes three parts: (a) We realize range image recovery based on the signal photon number histogram with a match filtering algorithm by using the proposed normalized discrete waveform functions. (b) We propose a two-dimensional double-threshold approximation denoising algorithm, which is used for image-level denoising. (c) The neighborhood threshold smoothing algorithm is proposed to supplement the target missing pixels. It is verified by Monte Carlo simulation and experimental results that the proposed algorithm has a high recovery ratio, denoising, and fidelity performance under few-frames detection in different SNRs conditions. The experimental largest target recovery ratio of the proposed algorithm is about 92%, the peak signal-to-noise ratio exceeds 16.4, and the maximum average measure of structural similarity is more than 85% under 500 statistical frames. This paper provides a new processing scheme for GM-APD lidar range image recovery under few-frames detection and low SNR, it lays the foundation for real-time and all-day monitoring.

Listening Effort in Hearing Aid Users: Is It Related to Hearing Aid Use and Satisfaction?

Listening effort is primarily reflective of real-world performance. Therefore, it is crucial to evaluate the listening effort to predict the performance of hearing aid (HA) users in their daily lives. This study aimed to investigate the relationship between listening effort, daily HA use time, and HA satisfaction. This is a cross-sectional study. Thirty-three bilateral behind-the-ear HA users (17 females and 16 males) between 19 and 37 years were participated. All participants had bilateral, symmetric, moderate sensorineural hearing loss and at least 6 months of experience using HAs. The pure-tone average thresholds (PTA) of the participants' left and right ears were 55.34 ± 4.38 and 54.85 ± 5.05, respectively. First, daily HA use times of the last 30 days were derived from data logging. Second, participants were asked to fill in the Satisfaction with Amplification in Daily Life Scale questionnaire (SADL). Lastly, participants performed the dual-task paradigm to evaluate listening effort. The dual-task paradigm consisted of a primary speech recognition task that included three different individualized signal-to-noise ratio (SNR) conditions, that is, SNR100, SNR80, and SNR50, which the participant could understand 100, 80, and 50% of the speech, respectively. The secondary task was a visual reaction time task that required participants to press the key in response to a visual probe (an image of a white or red rectangle). Multiple linear regression analyses were used to model the effect of factors (daily HA use time and HA satisfaction) on reaction times (RT) of each three individualized SNR sessions. Mean daily HA use time of the participants was 5.72 ± 4.14 hours. Mean RTs of SNR50, SNR80, and SNR100 conditions were 1,050.61 ± 286.49, 893.33 ± 274.79, and 815.45 ± 233.22 ms, respectively. Multiple linear regression analyses showed that daily HA use time and HA satisfaction are significantly related to listening effort in all SNR conditions. For SNR80 condition; F (2,30) = 47.699, p < 0.001, with an adjusted R 2 of 0.745. As far as we know, this study is the first to demonstrate a strong link between listening effort, daily HA use time, and HA satisfaction. Evaluating listening effort following the HA fitting session may provide preliminary information about the treatment success of HA.

Impact of Effortful Word Recognition on Supportive Neural Systems Measured by Alpha and Theta Power.

The goal of this study was to use theta and alpha electroencephalography (EEG) frequency power and self-report measures to examine performance monitoring, cognitive inhibition, and perceived effort required for speech understanding in noise. It was hypothesized that with a linear increase in word recognition task difficulty, there would be a linear increase in listening effort and word recognition performance would decrease in the challenging conditions. In addition, theta and alpha power would have an inverted U-shape across easy to challenging listening conditions. The inverted U-shape would reflect the neural underpinnings of listening effort that cannot be measured by task performance alone. EEG data were collected in 34 normal-hearing adults (18 to 33 years old) during the Words-In-Noise (WIN) test, which was presented in sound field. EEG frequency data were averaged and analyzed at three frontal channels for theta power (4 to 8 Hz), which is thought to reflect performance monitoring, and three parietal channels for alpha power (8 to 12 Hz), which is thought to reflect cognitive inhibition. A ten-point visual analog scale was administered after each WIN signal-to-noise ratio (SNR) condition to capture self-reported required and invested listening effort (RLE and ILE, respectively). The WIN SNR conditions were presented in descending and random order. The SNR presentation (descending or random SNR) had a null effect on word recognition performance; however, presentation did have an effect on theta power, alpha power, and ILE. When controlling for presentation, there were significant effects of SNR and presentation on both theta and alpha frequency power. Theta and alpha power had an inverted U-shape as a function of SNR from easy to challenging, with peak power in the moderate SNR conditions. RLE and ILE both significantly increased as task difficulty increased as expected; however, RLE showed a stronger relation to task performance than ILE. Alpha power was a significant predictor of RLE, ILE, and WIN performance when controlling for SNR. The elevated theta and alpha power in the easy to moderate SNRs and alpha power predicting self-reported listening effort suggest the activation of supportive neural systems during word recognition that could be considered a marker of listening effort. Moreover, the measures of neural support systems and listening effort were independent from task performance, which is a key element to further understanding the neural bases for listening effort. In the context of the broader literature, these results are consistent with (1) a parietal alpha role in supporting inhibitory control to suppress irrelevant information and (2) a frontal theta role in supporting performance monitoring in difficult listening conditions where speech recognition is feasible.

ERP Evidence of Working Memory Capacity and Signal-to-Noise Ratio Effects on Sentence Plausibility Tasks in Elderly Adults

Objectives: The purpose of this study is to evaluate the effect of Signal to Noise Ratio (SNR) and working memory capacity on the auditory processing of the elderly by comparing the average amplitude of event-related potentials (ERP) between groups in the sentence plausibility judgment task under the control of SNR.Methods: A total of 26 elderly people participated in this study, and based on the results of the working memory test, were divided into high a working memory (high WM) group (N= 13) and a low working memory (low WM) group (N= 13). The sentence stimuli consisted of plausible and implausible sentences. Implausible sentences were manipulated to cause a semantic violation in the verb. A white noise was added to the portion of the predicate to be –5 dB, 0 dB, and 5 dB SNR in the recorded sentence.Results: In the behavioral analysis, differences between the SNRs were significant. –5 dB SNR conditions revealed inactive performance, confirming that noise is a variable that increases cognitive load and difficulty in auditory processing. In the ERP analysis, the mean amplitude of the high WM group was significantly greater than the low WM group, and a distinct difference in N400 components between the groups was also observed in the grand average waveform graph.Conclusion: Intergroup differences were more evident in the conditions of SNR, which requires more listening effort. However, this may show that the low WM group had an inferior ability to detect the cognition of semantic implausibility in real time compared to the high WM group.

Evidence for cortical adjustments to perceptual decision criteria during word recognition in noise

Extensive increases in cingulo-opercular frontal activity are typically observed during speech recognition in noise tasks. This elevated activity has been linked to a word recognition benefit on the next trial, termed “adaptive control,” but how this effect might be implemented has been unclear. The established link between perceptual decision making and cingulo-opercular function may provide an explanation for how those regions benefit subsequent word recognition. In this case, processes that support recognition such as raising or lowering the decision criteria for more accurate or faster recognition may be adjusted to optimize performance on the next trial. The current neuroimaging study tested the hypothesis that pre-stimulus cingulo-opercular activity reflects criterion adjustments that determine how much information to collect for word recognition on subsequent trials. Participants included middle-age and older adults (N = 30; age = 58.3 ± 8.8 years; m ± sd) with normal hearing or mild sensorineural hearing loss. During a sparse fMRI experiment, words were presented in multitalker babble at +3 dB or +10 dB signal-to-noise ratio (SNR), which participants were instructed to repeat aloud. Word recognition was significantly poorer with increasing participant age and lower SNR compared to higher SNR conditions. A perceptual decision-making model was used to characterize processing differences based on task response latency distributions. The model showed that significantly less sensory evidence was collected (i.e., lower criteria) for lower compared to higher SNR trials. Replicating earlier observations, pre-stimulus cingulo-opercular activity was significantly predictive of correct recognition on a subsequent trial. Individual differences showed that participants with higher criteria also benefitted the most from pre-stimulus activity. Moreover, trial-level criteria changes were significantly linked to higher versus lower pre-stimulus activity. These results suggest cingulo-opercular cortex contributes to criteria adjustments to optimize speech recognition task performance.

Reliability Demodulation Algorithm Design for Phase Generated Carrier Signal

Phase-generated carrier (PGC) demodulation technology has been widely used in fiber-optic interferometric sensors system in recent years. Nonlinear errors caused by interference noise and parasitic amplitude modulation (AM) are key factors that affect the reliability of traditional PGC demodulation system based on arctangent (PGC-Arctan) algorithm. In order to enhance the reliable performance of PGC demodulation system under the conditions of low signal-to-noise ratio (SNR) and high parasitic AM interference, an ellipse fitting algorithm based on the Gauss–Newton iteration (GNI) is proposed, which is called the PGC-Arctan-GNI demodulation algorithm. The proposed algorithm uses the Euclidean distance to accurately estimate the geometric parameters of ellipse, which can correct the nonlinear distortion of the demodulated signal. In addition, the PGC demodulation system designed based on GNI algorithm has the advantages of strong antinoise ability, good antiparasitic AM ability, and widely dynamic range of the input signal amplitude. Experimental results show that, compared with the direct demodulation algorithm and the ellipse fitting algorithm based on the least squares (LS) method, the SINAD value of the proposed algorithm is always greater than 30 dB and higher than the other two algorithms under the condition of low SNR. Under the condition that the parasitic AM index <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> changes from 0 to 0.5, the signal-to-noise and distortion ratio (SINAD) curve of the proposed algorithm is more stable than the curves of the other two algorithms, and the fluctuation range of the SINAD value does not exceed 3 dB. This algorithm also has a wider input signal amplitude range than the LS algorithm. Especially, when the amplitude of the measured signal is less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pi$</tex-math></inline-formula> /2, the relative amplitude error of the GNI algorithm is less than 2% and significantly smaller than the LS algorithm. Finally, the demodulation results using real measured data in the actual system show that the frequency and amplitude of the demodulated signal are same as the original signal. At the same time, the SNIAD value of this system reaches 67.40 dB, the spurious-free dynamic range value reaches 68.82 dB, and the total harmonic distortion value reaches -68.40 dB@1 KHz. In summary, the proposed PGC-Arctan-GNI algorithm can eliminate system nonlinear errors caused by the parasitic AM, harmonics, and noise interference by estimating the ellipse parameters, thereby effectively enhancing the stability and reliability of the PGC demodulation system.

Sound source localization of non-synchronous measurements beamforming based on the truncated nuclear norm regularization

Due to the size and aperture limitations of the microphone array, poor resolution and limited working frequency range are challenging for acoustic imaging with beamforming methods. In this work, two fast iteration algorithms, alternating direction method of multipliers (ADMM) and accelerated proximal gradient line search method (APGL) based on truncated nuclear norm regularization (TNNR), are proposed to complete the cross-spectral matrix for non-synchronous measurements beamforming. Compared with current matrix completion algorithm based on nuclear norm minimization (NNM), TNNR is regarded as a better approximation to the rank function, which means it can approach the real solution better and restore the sound sources accurately at a low signal-to-noise ratio (SNR) environment. Among the TNNR-APGL and TNNR-ADMM, the TNNR-APGL algorithm has a lower matrix completion error and a shorter calculation time. The localization results of TNNR-ADMM method are more robust to the number of measurements. The experiment results show that TNNR methods greatly improve the resolution of conventional beamforming (CBF) and suppress the sidelobes under low SNR conditions, which indicates that the non-synchronous measurements beamforming based on TNNR methods has the potential to be applied in the industrial scene and low SNR environment.

Robust wide-range chirp rate measurement based on a flexible photonic fractional Fourier transformer.

We propose a photonic-assisted approach to measure the chirp rate of a linear frequency modulation waveform (LFMW) with a long duration, based on tunable photonic fractional Fourier transform (FrFT). Since the FrFT order can be continuously tuned by varying the frequency shift in an optical frequency-shifting loop (FSL), a specific FrFT order leads the fundamental frequency component arising in the output electrical spectrum to reach its maximum value, after the photonic-to-electrical conversion. Based on the designated FrFT order and the corresponding fundamental frequency in the output electrical spectrum, the chirp rate measurement over a wide range can be accessed, even the signal-to-noise ratio (SNR) of the input LFMW is substantially low. Simulation results indicate that the chirp rate of a 0.16-ms LFMW over a frequency range from 20 GHz to 26 GHz can be precisely characterized, with a relative measurement error of less than 0.13%, under the SNR condition of 0 dB. Moreover, an unambiguous chirp-rate measurement within the range of -5200 MHz/µs to 550 MHz/µs can be achieved. Hence, the proposed chirp rate measurement is featured with broadband operation, robust noise tolerance, low-frequency detection, and long-duration LFMW characterization.

A Phase-Derived Velocity Measurement Method Based on the Generalized Radon–Fourier Transform With a Low SNR

The phase-derived velocity measurement (PDVM) technique can achieve a high measurement accuracy at the phase level and thus has great application prospects in the field of micromotion feature extraction and target recognition. To achieve a PDVM with a low signal-to-noise ratio (SNR), a PDVM method based on the generalized Radon&#x2013;Fourier transform (GRFT) is proposed in this article. The main challenges that we overcome are phase extraction and phase ambiguity resolving under the condition of a low SNR. By utilizing the GRFT to estimate the target motion parameters, the echo peak position can be reconstructed, and then the peak phase value can be extracted. In the meantime, the phase ambiguity integer can be resolved based on the rough velocity estimation results obtained by the GRFT, and the phase ambiguity resolving can be realized at a low SNR. In addition, to suppress the influence of noise on the extracted phase, a filter design method based on the target motion characteristics is proposed to further improve the accuracy of the PDVM. In the simulation, the performance of the proposed method under different motion models and different SNR conditions is analyzed, and the effectiveness of the proposed method under low-SNR conditions is verified. Compared with directly using the GRFT, the proposed method has the advantages of strong applicability to different motion models and low computational load.

Automatic Extraction of Seismic Data Horizon Across Faults

Most of the traditional horizon interpretation methods are based on the dip of the seismic data. Since the dip does not correctly reflect the discontinuous reflection characteristics of the events, these methods usually cannot extract the horizons containing faults well. Meanwhile, the field seismic data observed in complex areas are generally with low signal-to-noise ratio (SNR), which makes the extraction of horizons difficult. To address these problems, an automatic horizon extraction approach under fault control is investigated in this article. First, the horizon dip is obtained by using plane wave destruction (PWD), and the dip information can be utilized to control the overall trend of the horizon curve. Based on the dip information, we can also calculate the weight coefficients to characterize the faults. The fault characterization results can better guide the horizon extraction. Then, the dynamic time warping (DTW) is used for phase matching to further obtain the similar slope and describe the details of the horizontal curve. Finally, the horizon extraction equation is developed with the dip and phase information as the guide and solved iteratively to obtain the final horizon curve. The effectiveness of the presented approach is tested by both synthetic and field data. The results demonstrate that the presented approach can accurately pick the horizon information across faults and is more stable in the condition of low SNR compared with previous methods.