Low SNR Conditions Research Articles

A novel multi‐target TBD scheme for GNSS‐based passive bistatic radar

AbstractGlobal Navigation Satellite System (GNSS)‐based passive bistatic radar (PBR) systems hold promise for use as low‐altitude surveillance mechanisms in critical urban and suburban zones, attributed to their low power consumption, good concealment, and worldwide reach. However, the increasing demand for airspace regulation presents challenges for multi‐target tracking. Additionally, the limited power budget of GNSS signals results in low target SNR, restricting the detection range. Hence, a novel multi‐target track‐before‐detect (TBD) scheme is proposed. This strategy employs a dual‐channel coarse focusing (DCCF)‐based cluster centroid extraction algorithm to identify potential target information in the range Doppler (RD) domain. Subsequently, a modified cardinalised probability hypothesis density (MCPHD) filter is utilised, enhanced with a birth target intensity estimation module assisted by Doppler and a trajectory management module, to accurately track multiple targets under conditions of low SNR. Simulation results and performance analysis using the Optimal Sub‐pattern Assignment (OSPA) metric confirm the effectiveness of our approach. Furthermore, in a real‐world experiment using the GPS L5 signal as an illuminator, the authors successfully processed experimental data to track a civil aircraft over 10 frames, demonstrating the practical applicability of the proposed method in GNSS‐based PBR systems.

Improved Dipole Source Localization from Simultaneous MEG-EEG Data by Combining a Global Optimization Algorithm with a Local Parameter Search: A Brain Phantom Study.

Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.

A Deep-Learning-Based Method for Spectrum Sensing with Multiple Feature Combination

Cognitive radio networks enable the detection and opportunistic access to an idle spectrum through spectrum-sensing technologies, thus providing services to secondary users. However, at a low signal-to-noise ratio (SNR), existing spectrum-sensing methods, such as energy statistics and cyclostationary detection, tend to fail or become overly complex, limiting their sensing accuracy in complex application scenarios. In recent years, the integration of deep learning with wireless communications has shown significant potential. Utilizing neural networks to learn the statistical characteristics of signals can effectively adapt to the changing communication environment. To enhance spectrum-sensing performance under low-SNR conditions, this paper proposes a deep-learning-based spectrum-sensing method that combines multiple signal features, including energy statistics, power spectrum, cyclostationarity, and I/Q components. The proposed method used these combined features to form a specific matrix, which was then efficiently learned and detected through the designed ‘SenseNet’ network. Experimental results showed that at an SNR of −20 dB, the SenseNet model achieved a 58.8% spectrum-sensing accuracy, which is a 3.3% improvement over the existing convolutional neural network model.

Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling.

Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a 2D linear-combination model (2D-LCM) of individual transients ("model-based FPC"). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates. We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the SD of those ground-truth errors, and amplitude Cramér Rao lower bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data. 2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of FPC and amplitudes performed substantially better at low-to-very-low SNR. Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, for example, long TEs or strong diffusion weighting.

Enhancing multi-target detection in low SNR conditions

Enhancing multi-target detection in low SNR conditions

A Novel Joint Motion Compensation Algorithm for ISAR Imaging Based on Entropy Minimization.

Space targets move in orbit at a very high speed, so in order to obtain high-quality imaging, high-speed motion compensation (HSMC) and translational motion compensation (TMC) are required. HSMC and TMC are usually adjacent, and the residual error of HSMC will reduce the accuracy of TMC. At the same time, under the condition of low signal-to-noise ratio (SNR), the accuracy of HSMC and TMC will also decrease, which brings challenges to high-quality ISAR imaging. Therefore, this paper proposes a joint ISAR motion compensation algorithm based on entropy minimization under low-SNR conditions. Firstly, the motion of the space target is analyzed, and the echo signal model is obtained. Then, the motion of the space target is modeled as a high-order polynomial, and a parameterized joint compensation model of high-speed motion and translational motion is established. Finally, taking the image entropy after joint motion compensation as the objective function, the red-tailed hawk-Nelder-Mead (RTH-NM) algorithm is used to estimate the target motion parameters, and the joint compensation is carried out. The experimental results of simulation data and real data verify the effectiveness and robustness of the proposed algorithm.

Enhanced Underwater Single Vector-Acoustic DOA Estimation via Linear Matched Stochastic Resonance Preprocessing

Underwater acoustic vector sensors (UAVSs) are increasingly utilized for remote passive sonar detection, but the accuracy of direction-of-arrival (DOA) estimation remains a challenging problem, particularly under low signal-to-noise ratio (SNR) conditions and complex background noise. In this paper, a comprehensive theoretical analysis is conducted on UAVS signal preprocessing subjected to gain-phase uncertainties for average acoustic intensity measurement (AAIM) and complex acoustic intensity measurement (CAIM)-based vector DOA estimation, aiming to explain the theoretical restrictions of intensity-based vector acoustic preprocessing approaches. On this basis, a generalized vector acoustic preprocessing optimization model is established in which the principle can be described as “maximizing the denoising performance under the constraints of an equivalent amplitude-gain response and phase-bias response”. A novel vector acoustic preprocessing method named linear matched stochastic resonance (LMSR) is proposed within the framework of matched stochastic resonance theory, which can naturally guarantee the linear gain-phase restrictions, as well achieving effective denoising performance. Numerical analyses demonstrate the superior vector DOA estimation performance of our proposed LMSR-AAIM and LMSR-CAIM methods in comparison to classical intensity-based AAIM and CAIM methods, especially under low-SNR conditions and non-Gaussian impulsive noise circumstances. Experimental verification conducted in the South China Sea further verifies its the effectiveness for practical application. This work can lay a solid foundation to break through the challenges of underwater remote vector acoustic DOA estimation under low-SNR conditions and complex ocean ambient noise and can provide important guidance for future research work.

Attention-Enhanced Dual-Branch Residual Network with Adaptive L-Softmax Loss for Specific Emitter Identification under Low-Signal-to-Noise Ratio Conditions

To address the issue associated with poor accuracy rates for specific emitter identification (SEI) under low signal-to-noise ratio (SNR) conditions, where the single-dimension radar signal characteristics are severely affected by noise, we propose an attention-enhanced dual-branch residual network structure based on the adaptive large-margin Softmax (ALS). Initially, we designed a dual-branch network structure to extract features from one-dimensional intermediate frequency data and two-dimensional time–frequency images, respectively. By assigning different attention weights according to their importance, these features are fused into an enhanced joint feature for further training. This approach enables the model to extract distinctive features across multiple dimensions and achieve good recognition performance even when the signal is affected by noise. In addition, we have introduced L-Softmax to replace the original Softmax and propose the ALS. This approach adaptively calculates the classification margin decision parameter based on the angle between samples and the classification boundary and adjusts the margin values of the sample classification boundaries; it reduces the intra-class distance for the same class while increasing the inter-class distance between different classes without the need for cumbersome experiments to determine the optimal value of decision parameters. Our experimental findings revealed that, in comparison to alternative methods, our proposed approach markedly enhances the model’s capability to extract features from signals and classify them in low-SNR environments, thereby effectively diminishing the influence of noise. Notably, it achieves the highest recognition rate across a range of low-SNR conditions, registering an average increase in recognition rate of 4.8%.

Effective Signal Extraction Algorithm for Cerebral Blood Oxygen Based on Dual Detectors.

Functional near-infrared spectroscopy (fNIRS) can dynamically respond to the relevant state of brain activity based on the hemodynamic information of brain tissue. The cerebral cortex and gray matter are the main regions reflecting brain activity. As they are far from the scalp surface, the accuracy of brain activity detection will be significantly affected by a series of physiological activities. In this paper, an effective algorithm for extracting brain activity information is designed based on the measurement method of dual detectors so as to obtain real brain activity information. The principle of this algorithm is to take the measurement results of short-distance channels as reference signals to eliminate the physiological interference information in the measurement results of long-distance channels. In this paper, the performance of the proposed method is tested using both simulated and measured signals and compared with the extraction results of EEMD-RLS, RLS and fast-ICA, and their extraction effects are quantified by correlation coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE). The test results show that even under low SNR conditions, the proposed method can still effectively suppress physiological interference and improve the detection accuracy of brain activity signals.

Passive localization using horizontal dual-array acoustic intensity striations

The formation of interference striations is closely related to channel structure and target location, which has attracted extensive attention from scholars at home and abroad. In this paper, the passive localizing method using observed interference striations of horizontal dual-arrays will be studied. The relationship between interference phenomena and target motion is discussed firstly, then the basic principle of the passive positioning method by using horizontal dual-array acoustic intensity striations is derived mathematically. Meanwhile, it can be seen that the shape of the interference striations is related to the parameters, which reflects the motion parameters of the target. This provides mathematical theory support for extracting the the target motion state parameters carried out by Hough transformation according to these acoustic interference striations.The simulation results show that the proposed method can effectively obtain the targets’ range. Meanwhile, comparing the ranging results of different groups of parameters, we can see that the more accurate the target motion parameters are, the smaller the estimated target distance error is. Finally, the performance of the algorithm under different noise conditions is discussed. The simulation results show that the error of extracted parameters is larger under the condition of low SNR, which leads to a larger passive positioning error. However, at high SNR, the range estimation performance is good, within 10%.

Source localization in deep ocean based on complex convolutional neural network

To solve the problem that phase information cannot be used effectively in underwater acoustic localization, a deep learning network based on complex convolutional neural network is proposed in this paper. The complex convolution layer is used to effectively utilize the phase features favorable to the source localization problem, and the phase information is used to improve the feature extraction performance. Through simulation, the performance of the network for source localization in deep-sea direct arrival region under different SNR conditions is analyzed. The results show that the complex convolutional network proposed in this paper can locate the sound source with less computation and has better performance under the condition of low SNR.

Boosting Open-Set RF Signal Recognition Under Low SNR Condition in Feature Space

Boosting Open-Set RF Signal Recognition Under Low SNR Condition in Feature Space

Simulation-Enhanced MQAM Modulation Identification in Communication Systems: A Subtractive Clustering-Based PSO-FCM Algorithm Study

Signal modulation recognition is often reliant on clustering algorithms. The fuzzy c-means (FCM) algorithm, which is commonly used for such tasks, often converges to local optima. This presents a challenge, particularly in low-signal-to-noise-ratio (SNR) environments. We propose an enhanced FCM algorithm that incorporates particle swarm optimization (PSO) to improve the accuracy of recognizing M-ary quadrature amplitude modulation (MQAM) signal orders. The process is a two-step clustering process. First, the constellation diagram of the received signal is used by a subtractive clustering algorithm based on SNR to figure out the initial number of clustering centers. The PSO-FCM algorithm then refines these centers to improve precision. Accurate signal classification and identification are achieved by evaluating the relative sizes of the radii around the cluster centers within the MQAM constellation diagram and determining the modulation order. The results indicate that the SC-based PSO-FCM algorithm outperforms the conventional FCM in clustering effectiveness, notably enhancing modulation recognition rates in low-SNR conditions, when evaluated against a variety of QAM signals ranging from 4QAM to 64QAM.

Waveform and Receiver Design for OTFS-based SAR under Interference and Low SNR Conditions

Waveform and Receiver Design for OTFS-based SAR under Interference and Low SNR Conditions

Accurate piecewise centroid calculation algorithm for wavefront measurement in adaptive optics.

Adaptive optics using direct wavefront sensing (direct AO) is widely used in two-photon microscopy to correct sample-induced aberrations and restore diffraction-limited performance at high speeds. In general, the direct AO method employs a Sharked-Hartman wavefront sensor (SHWS) to directly measure the aberrations through a spot array. However, the signal-to-noise ratio (SNR) of spots in SHWS varies significantly within deep tissues, presenting challenges for accurately locating spot centroids over a large SNR range, particularly under extremely low SNR conditions. To address this issue, we propose a piecewise centroid calculation algorithm called GCP, which integrates three optimal algorithms for accurate spot centroid calculations under high-, medium-, and low-SNR conditions. Simulations and experiments demonstrate that the GCP can accurately measure aberrations over a large SNR range and exhibits robustness under extremely low-SNR conditions. Importantly, GCP improves the AO working depth by 150 µm compared to the conventional algorithm.

An improved deep learning approach for speech enhancement

Single-channel speech enhancement refers to the task of improving the quality and intelligibility of a speech signal in a noisy environment. Time-domain and time-frequency-domain methods are two main categories of approaches for speech enhancement. In this paper, we propose a approach based on a cross-domain framework. This framework utilizes our knowledge of the spectrogram and overcomes some of the limitations faced by time-frequency domain methods. First, we apply the intrinsic mode functions of the empirical mode decomposition and an improved version of principal component analysis. Then, we design a cross-domain learning framework to determine the correlations along the frequency and time axes. At low SNR = -5 dB, the effectiveness of our proposed approach is demonstrated by its performance based on objective and subjective measures. With average scores of -0.49, 2.47, 2.44, and 0.68 for SegSNR, PESQ, Cov, and STOI, respectively. The results highlight the success of our approach in addressing low SNR conditions.

Accurate channel estimation and hybrid beamforming using Artificial Intelligence for massive MIMO 5G systems

In a large-scale massive Multi User-Multiple Input Multiple Output (MU-MIMO) environment channel estimation and beamforming is a breathtaking task for enhancing the array gain without utilizing the many Radio Frequency (RF) chains. Somehow, several state-of-the-art works perform channel estimation and Hybrid Beamforming (HB) using Artificial Intelligence (AI) Algorithms but the massive computation intricacy and power consumption hindered the performance of the existing system. By considering the existing issues, we designed a DL-based Hybrid Beamformer for the MIMO environment with 5G communication technology (DLHB-MIMO 5G). The proposed HB design centers on three progressive processes such as accurate channel estimation, hybrid beamformer design, and hybrid beamforming. In the accurate channel estimation phase, the noise and interference-free channels are estimated using the Improved Extreme Learning Machine-Adaptive Orthogonal Matching Pursuit (IELM-AOMP) algorithm based on channel parameters and user feedback. In the HB design stage, the shortcoming of prior DL models is resolved by adopting Transfer Learning Lite Convolutional Neural Network (TL-LiteCNN) for designing a hybrid beamformer. Beforehand, we select the appropriate antenna numbers using Stackelberg Game Theory (StGT) using adequate parameters. In the hybrid beamforming stage, the problem of less Spectral Efficiency (SE) during low SNR conditions is fixed by adopting the Improved Proximal Policy Optimization (IPPO) algorithm with several beamforming parameters to generate highly resourceful hybrid beams. The realization of the proposed research is carried out using the MATLAB R2020a simulation tool and the performance of the proposed work is compared with the major state-of-the-art works in terms of useful performance metrics. The comparative results show that the proposed work beats the existing works.

Cavitation Diagnostics Based on Self-Tuning VMD for Fluid Machinery with Low-SNR Conditions

Variational mode decomposition (VMD) is a suitable tool for processing cavitation-induced vibration signals and is greatly affected by two parameters: the decomposed number K and penalty factor α under strong noise interference. To solve this issue, this study proposed self-tuning VMD (SVMD) for cavitation diagnostics in fluid machinery, with a special focus on low signal-to-noise ratio conditions. A two-stage progressive refinement of the coarsely located target penalty factor for SVMD was conducted to narrow down the search space for accelerated decomposition. A hybrid optimized sparrow search algorithm (HOSSA) was developed for optimal α fine-tuning in a refined space based on fault-type-guided objective functions. Based on the submodes obtained using exclusive penalty factors in each iteration, the cavitation-related characteristic frequencies (CCFs) were extracted for diagnostics. The power spectrum correlation coefficient between the SVMD reconstruction and original signals was employed as a stop criterion to determine whether to stop further decomposition. The proposed SVMD overcomes the blindness of setting the mode number K in advance and the drawback of sharing penalty factors for all submodes in fixed-parameter and parameter-optimized VMDs. Comparisons with other existing methods in simulation signal decomposition and in-lab experimental data demonstrated the advantages of the proposed method in accurately extracting CCFs with lower computational cost. SVMD especially enhances the denoising capability of the VMD-based method.

LFM based Wideband DOA Estimation using Deep Neural Network at Low SNR

This work focuses on deep learning-based wideband direction-of-arrival (DoA) estimation for a wideband in particular LFM in case of extreme noise. We propose a convolutional neural network (CNN) that utilizes the correlation matrix to estimate and trained using multi-channel data in low SNR conditions. By using a systematic approach and treating the problem as a way to identify multiple possible DoAs, the CNN is trained to predict DoAs under different SNR conditions. This allows the CNN to accurately estimate the directions from which signals are coming, regardless of the level of noise in the environment. The architecture proposed exhibits robustness to noise, works effectively with a small number of snapshots, and achieves high resolution in angle estimation. Experimental findings demonstrate notable enhancements in performance under low SNR conditions when compared to existing methods, without the need for parameter tuning for correlated and uncorrelated sources. The enhanced robustness of our solution has broad applications in various fields, including wireless array sensors, acoustic microphones, and sonars.

A three-dimensional microseismic downhole noise suppression based on polarization filtering method in Shearlet transform

Microseismic noise suppression is widely used in the exploration of unconventional oil and gas resources. The effective microseismic downhole signals have extremely weak energy and are contaminated by strong interference, making data processing and interpretation difficult. The need for high-frequency effective signal reservation presents a basic problem in the design of noise suppression methods. The effective signals represent as the continuous reflection event and have more concentrated features in the transform domain, which can be used to tell the signal from the irregular microseismic noise. However, the high-frequency signal and extremely complex noise bring difficulty in accurately separating them by a single threshold. In this study, we propose a novel denoising method called Shearlet-polarization filtering to effectively suppress the microseismic noise. In general, Shearlet-polarization filtering is the combination of polarization filtering and conventional Shearlet transform. Specifically, the Shearlet transform can decompose the microseismic data into multi-directional and multi-scale information, providing a solid foundation for the separation of effective signals and background noise. From this basis, polarization filtering achieves signal reservation and noise attenuation by making full use of the three-dimensional information. To evaluate the performance, we also compare the proposed method with conventional Shearlet threshold filtering and polarization filtering. Experimental results both in synthetic and field data processing indicate that the Shearlet-polarization filtering is superior to the competing methods because it can significantly improve the continuity and smoothness of the microseismic events, even in low SNR conditions.