Low Signal-to-noise Ratio Environment Research Articles

Performance Analysis of Cooperative Spectrum Sensing using Empirical Mode Decomposition and Artificial Neural Network in Wireless Regional Area Network

Background: Radio spectrum is natural and the most precious means in wireless communication systems. Optimal spectrum utilization is a key concern for today's cutting-edge wireless communication networks. The impending problem of the lack of available spectrum has prompted the development of a new idea called “Cognitive Radio” (CR). Cooperative spectrum sensing (CSS) is utilized to improve the detection performance of the system. Several fusion algorithms of decision-making are proposed for sensing the licensed user, but they do not work well under low signal-to-noise ratio (SNR). Objectives: To address the issue of poor detection performance under low SNR, Empirical mode decomposition (EMD) and artificial neural network (ANN) based CSS under Rayleigh multipath fading channel in IEEE 802.22 wireless regional area network (WRAN) is proposed in this paper. Method: In this work, we propose the use of ANN as a fusion center. First, the received signal's energy is calculated using EMD. The computed energy, SNR, and false alarm probability are combined to form a data set of 2048 samples. They are utilized to train Levenberg- Marquardt back propagation training algorithm-based feed-forward neural network (FFNN). Using this trained network, CSS in WRAN is simulated under Rayleigh multipath fading. Results: Simulation results show that the proposed CSS method based on EMD-ANN outperforms the standard fast Fourier transform (FFT) and EMD detection-based cooperative spectrum sensing with a hard "OR" fusion at low SNR. With Pf =0.01, 100% detection accuracy with proposed techniques is obtained at SNR= -22dB. Conclusion: The findings show that the suggested approach outperforms EMD and FFT based energy detection scheme-based traditional CSS in low SNR environments.

Dual-Branch feature fusion residual neural network for individual radar signal recognition in low SNR environments

Abstract To address the challenge of radar emitter signal individual recognition, where single-dimensional radar fingerprint features are susceptible to noise interference and neural networks exhibit low recognition accuracy in low signal-to-noise ratio (SNR) environments, this study introduces a residual neural network predicated on a two-branch feature fusion. This approach amalgamates two-dimensional time-frequency domain features with one-dimensional intermediate-frequency signal features. Unlike existing algorithms, our proposed method integrates the knowledge gleaned from features learned across different dimensions. This integration enables the neural network to utilize features from multiple dimensions for recognition, thereby mitigating the impact of noise on a single feature. Experimental results demonstrate that our proposed algorithm outperforms others under various low SNRs, achieving an average recognition rate of 88.39%.

A RSBU-LSTM network for radio frequency fingerprint identification relying on multiple features

Radio frequency fingerprint identification (RFFI) can distinguish highly similar wireless communication devices to protect physical layer security and improve the security of wireless networks effectively, which has been widely used for spectrum management and physical layer secure communication. However, most RFFI methods show a degradation of performance under low signal-to-noise ratio (SNR) environments. In this paper, we propose a RSBU-LSTM network relying on multiple features to improve the identification accuracy with low SNR. Firstly, we use multiple features of in-phase (I), quadrature (Q), and phase as inputs. Then, we use multiple Residual Shrinkage Building Units (RSBUs) to extract the correlation features within the cycle of signals and preserve as many features as possible in low SNR environments. Finally, we use the long short-term memory (LSTM) to extract the relevant features of the signals of non-adjacent cycles. The experimental results show that the proposed network can effectively complete RFFI in low SNR environments and show better performance than other models used for comparison.

Time-difference reassigned transform with application to time difference of arrival for impulsive signal

Time difference of arrival (TDOA) is essential in localization, communication, and navigation. Under ambient noise interference, the impulsive signal is transmitted over a long distance and reaches the sensor with a low signal-to-noise ratio (SNR). Aiming to achieve a precise time delay estimation in scenarios with low SNR, this paper extends the time-reassigned extracting transform (TRET) theory to TDOA and proposes a time difference rearrangement extracting transform (TDRT) algorithm. The implementation process of the proposed algorithm comprises three steps. Initially, a rough two-dimensional time delay estimation is obtained by calculating the partial derivative of the short-time cross-power spectrum with respect to the frequency variable. Secondly, a rearrangement operation separates the signal's time-frequency (TF) points from the noise. Thirdly, a refined TDOA estimation is obtained by inverse Fourier transforming TF points extracted from the time-delay energy ridge. Simulation results show that the TDRT algorithm is effective in time delay estimation. Furthermore, experimental results prove that the performance of the TDRT algorithm outperforms comparable algorithms in low SNR environments.

A cooperative spectrum sensing method based on semi-supervised clustering with variational mode decomposition and information geometry

In practice, obtaining a large number of sample labels for the supervised learning-based cooperative spectrum sensing (CSS) method is challenging. Additionally, the CSS method based on information geometry (IG) and clustering algorithm exhibited low detection performance in low signal-to-noise ratio (SNR) environment. This paper proposes a cooperative spectrum sensing method based on variational mode decomposition and IG with semi-supervised clustering. First, the secondary users (SUs) reconstruct the received signals using variational mode decomposition. Next, the fusion center combines all the reconstructed signals into a single matrix and calculates its corresponding covariance matrix. Finally, a binary classifier is obtained via training offline on the matrix manifold space using a semi-supervised clustering algorithm based on IG. The simulation results show that increasing the number of SUs and sampling points can improve the CSS performance of the proposed algorithm. Furthermore, the proposed algorithm is compared with other CSS algorithms, demonstrating its superior spectrum sensing performance regardless of whether the SUs are in the same very low SNR environment.

A robust and lightweight voice activity detection algorithm for speech enhancement at low signal-to-noise ratio

Voice Activity Detection (VAD) is a crucial component of Speech Enhancement (SE) for accurately estimating noise, which directly affects the SE effectiveness in improving speech quality. However, conventional non-data-driven VADs often suffer from decreased accuracy at a low signal-to-noise ratio (SNR). To address this issue, a multi-feature and cosine similarity-based multi-observation VAD algorithm (mVAD) are proposed in this study. This algorithm selects noise-robust features, with Mel-frequency Cepstral Coefficients (MFCCs) as the main features, and utilizes several optimization techniques and an adaptive threshold for background noise updating. Furthermore, the soft VAD results are smoothed with an improved exponential moving average (EMA) algorithm. Besides, a shifting window is utilized to track the mean value and obtain an adaptive threshold for converting the soft results to binary ones. Experimental results indicate that mVAD can maintain high classification accuracy down to -10 dB with an increment of approximately 28% while also being computationally efficient for the CPU time (about 1/3 of statistical model-based methods). It also maintained high robustness at SNRs less than 0 dB (Δ≤2.1%). Moreover, sometimes mVAD even achieved higher accuracy levels than deep learning-based VADs. To further demonstrate the effectiveness of the proposed method, the VAD results are used as an additional feature to train and test a neural network (NN)-based SE model, enhancing the SE performance. This study proves that mVAD does not rely on prior noise knowledge, reaching the dual effect of complexity reduction and accuracy improvement for speech enhancement, making it a promising approach for robust VAD in low SNR environments.

Enhancing Hybrid Spectrum Access in CR-IoT Networks: Reducing Sensing Time in Low SNR Environments

The current utilization of the licensed spectrum band is not optimal. the abundance of Internet of Things (IoT) gadgets could lead to congestion in the unlicensed spectrum band. A potential solution is to integrate cognitive radios into IoT devices, specifically by developing CR-IoT (cognitive-radio-enabled IoT) devices that leverage the hybrid spectrum access (HSA) technique to access the licensed spectrum band and employ energy detectors for spectrum sensing. While HSA can enable high data throughput for CR-IoT networks, environments with low signal-to-noise ratio (SNR) may experience reduced performance. Particularly, in low SNR environments with SNR values ranging from -20dB to -24dB, the high level of noise uncertainty can cause the energy detector to spend more time sensing, which results in a significant reduction in transmission time and overall throughput. In this study, a novel approach is presented to address the challenge of noise uncertainties on the energy detector by implementing an adaptive sensing threshold. The simulation outcomes reveal that the proposed technique can significantly improve the throughput in scenarios characterized by low signal-to-noise ratio (SNR), with a maximum enhancement of up to 28%. This innovative approach can pave the way for more robust and efficient energy detectors in the presence of noise uncertainties .

Adaptive cascade detection of weak magnetic anomalies based on marine predators algorithm-stochastic resonance

Magnetic anomaly detection is a new technology for underground or underwater ferromagnetic targets using the physical principle that ferromagnetic targets will be long-term magnetized by the geomagnetic field to generate abnormal magnetic fields. This technology is often used in underwater security, mineral exploration, and other areas. But it is quite challenging to effectively detect weak magnetic anomaly signals in a complex underwater environment. Therefore, we propose an adaptive cascade weak magnetic anomaly detection method based on Marine Predators Algorithm-Stochastic Resonance (MPA-SR). According to the characteristics of magnetic anomaly signals, the cascade detection method of low-pass filtering, stochastic resonance, and threshold detection is designed to improve the detection probability of magnetic anomaly signals. In addition, the Marine predator algorithm with optimized initialization strategy and step size control parameters is used to improve the stochastic resonance system to adaptively detect the magnetic anomaly signal in more applications. The simulation results show that the signal-to-noise ratio (SNR) of the output signal of the MPA-SR method is 2.41 dB higher than the input signal, and the detection probability of the method is 57% higher than that of the minimum entropy (ME) method under the same low SNR environment. The method can provide the theoretical basis and empirical reference for further application of magnetic anomaly data like identifying, locating, and tracking underwater magnetic targets.

A sound source localization method based on improved second correlation time delay estimation

The sound source localization (SSL) system based on the microphone array has important applications in audio and video conference, security monitoring and intelligent cockpit. However, the SSL method based on time difference of arrival is susceptible to ambient noise. Therefore, an improved second correlation delay estimation algorithm is proposed in this paper. The pure source signal is obtained by wavelet denoising, and then the time delay is calculated by the second correlation time delay estimation algorithm with the weighting functions of the smoothed coherence transform and the Roth processor. The position of the sound target is calculated from the time delay. Aiming at the moving target, an extended Kalman filter is introduced to track the moving trajectory of the sound source. The static and moving SSL simulations are conducted and the results of the proposed algorithm are compared with those of the single-weighted quadratic correlation (SQC) algorithm and the high-power quadratic correlation algorithm. The static sound source positioning errors of the proposed algorithm under −10 dB SNR are respectively 3.97 m and 5.86 m smaller than those of the HQC algorithm and the SQC algorithm. The moving SSL trajectory based on the proposed algorithm is still closest to the real track under −10 dB SNR. This indicates that the proposed algorithm has high precision and strong robustness for sound source location in the low signal-to-noise ratio (SNR) environment. In the experiment, the proposed algorithm can accurately calculate the direction of arrival (DOA) of static sound source and stably track DOA of moving sound source. This is consistent with the simulation results, which further verifies the effectiveness and practicability of the algorithm. This novel algorithm with high time delay estimation accuracy is of great significance for SSL in low SNR environment.

Perceptual Characteristics of Voice Identification in Noisy Environments

Auditory analysis is an essential method that is used to recognize voice identity in court investigations. However, noise will interfere with auditory perception. Based on this, we selected white noise, pink noise, and speech noise in order to design and conduct voice identity perception experiments. Meanwhile, we explored the impact of the noise type and frequency distribution on voice identity perception. The experimental results show the following: (1) in high signal-to-noise ratio (SNR) environments, there is no significant difference in the impact of noise types on voice identity perception; (2) in low SNR environments, the perceived result of speech noise is significantly different from that of white noise and pink noise, and the interference is more obvious; (3) in the speech noise with a low SNR (−8 dB), the voice information contained in the high-frequency band of 2930~6250 Hz is helpful for achieving accuracy in voice identity perception. These results show that voice identity perception in a better voice transmission environment is mainly based on the acoustic information provided by the low-frequency and medium-frequency bands, which concentrate most of the energy of the voice. As the SNR gradually decreases, a human’s auditory mechanism will automatically expand the receiving frequency range to obtain more effective acoustic information from the high-frequency band. Consequently, the high-frequency information ignored in the objective algorithm may be more robust with respect to identity perception in our environment. The experimental studies not only evaluate the quality of the case voice and control the voice recording environment, but also predict the accuracy of voice identity perception under noise interference. This research provides the theoretical basis and data support for applying voice identity perception in forensic science.

A Repetitive Spectrum Learning Framework for Monaural Speech Enhancement in Extremely Low SNR Environments (Student Abstract)

Monaural speech enhancement (SE) at an extremely low signal-to-noise ratio (SNR) condition is a challenging problem and rarely investigated in previous studies. Most SE methods experience failures in this situation due to three major factors: overwhelmed vocals, expanded SNR range, and short-sighted feature processing modules. In this paper, we present a novel and general training paradigm dubbed repetitive learning (RL). Unlike curriculum learning that focuses on learning multiple different tasks sequentially, RL is more inclined to learn the same content repeatedly where the knowledge acquired in previous stages can be used to facilitate calibrating feature representations. We further propose an RL-based end-to-end SE method named SERL. Experimental results on TIMIT dataset validate the superior performance of our method.

Range profile similarity‐oriented method for radar imaging of moving target under low signal‐to‐noise ratio environment

Inverse synthetic aperture radar (ISAR) imaging is an advanced technique for reconstructing high-resolution images of moving targets. However, noise interference degrades the readability of the ISAR images under low echo signal-to-noise ratio (SNR) environments. Furthermore, noise impairs the performance of ISAR motion compensation, which induces severe defocussing. Thus, a novel range profile similarity-oriented (RAP-SO) method with a two-step denoising strategy is presented. First, attributed to the coherence between ISAR range profiles, the coherent weighted template (CWT) is proposed to preliminarily remove the noise in a high-resolution range profile (HRRP) and restore the range profile similarity impaired by noise. In order to robustly complete range alignment under a low SNR environment, Keystone transform (KT) is utilised. Thereafter, the weighted template is constructed by virtue of coherent integration. The CWT adaptively gives different weights to HRRP signals and noise, which is called coarse denoising. Second, inspired by the similarities of range profiles, the minimum weighted nuclear norm (Min-WNN) optimisation is proposed to further reduce the residual noise. The coarse denoised HRRPs are rearranged into an approximate low-rank matrix. Subsequently, rank-constraint optimisation on the HRRP matrix via minimising the weighted nuclear norm is performed, aiming to efficiently suppress the residual noise. This step is called fine denoising. As a result, ISAR image quality is significantly improved. Comprehensive experiments illustrate the effectiveness and superiority of the presented method.

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.

Coherent Integration for Maneuvering Target Detection at Low SNR Based on Radon-General Linear Chirplet Transform

This letter considers the coherent integration problem for a maneuvering target in low signal-to-noise-ratio (SNR) circumstances. Focusing on the range migration (RM) and Doppler frequency migration (DFM) problems caused by the motion of the target, we propose a new method called Radon-general linear chirplet transform (RGLCT). Jointly motion parameters search is employed to obtain the trajectory of the maneuvering target and the coherent integration is achieved via general linear chirplet transform (GLCT). Because of the non-sensitive-to-noise feature of the GLCT, RGLCT can realize weak target coherent integration in very low SNR environments. Multi-target detection can be achieved successfully because the GLCT is not influenced by the cross-term components. Finally, simulations and real data experiments are performed to demonstrate the effectiveness of the method. The results show that the proposed method has superior detection ability than methods including Radon-Fourier transform (RFT), and Radon-Lv’s distribution (RLVD). Both theory and experiments have fully proved that the proposed method can effectively realize coherent integration in low SNR environments.

Joint Range-Doppler Estimation Based on Multipulse Processing of Composite Hyperbolic Frequency Modulated Waveforms

We herein investigate the joint estimation of range and Doppler based on multipulse processing of composite hyperbolic frequency-modulated (HFM) waveforms. The composite HFM waveform solves the range bias problem in the HFM waveform by using the mapping relationship between the radial velocity and the peak-to-peak interval in the outputs of matched filters. However, the result may be inaccurate in the presence of multiple false peaks. In this letter, we fit matched filter outputs into a hidden Markov model by utilizing the kinetic model of the target. We can subsequently solve the peak-pair selection problem and perform the joint range-Doppler estimation by using the Viterbi algorithm. The proposed method, by multipulse processing, lowers the signal-to-noise ratio (SNR) requirement, and exact detection of the target in each return pulse is no longer necessary. We prove the effectiveness of the proposed method via numerical simulations, and the simulation results demonstrate that this method outperforms existing methods in low SNR environments.

Fully Coherent Integration and Measurement of Optimized Frequency Agile Waveform for Weak Target High-Resolution ISAR Imaging

Frequency agile waveform effectively decreases interception probability and increases the anti-jamming ability for radar probing in the electromagnetic countermeasure environment. However, the transmitting agile frequency introduces jitter phases causing de-coherence of echoes, which decreases signal-to-noise ratio (SNR) accumulation gain and leads to difficult motion estimation. Therefore, stable target detection, motion parameters estimation, and inverse synthetic aperture radar (ISAR) imaging for frequency-agile radar are still intractable problems in the low SNR environment. Aiming at these problems, we proposed a fully coherent processing method for weak target detection and ISAR imaging for frequency agile waveform in this paper. The agile waveform with low range and Doppler side lobes is designed to improve motion parameters estimation and ISAR imaging performance. Then, the joint jitter phases and motion parameters estimation based on the generalized likelihood ratio test (GLRT) is proposed to realize robust target detection and motion parameters estimation in low SNR conditions. The translational motion compensation and sparse ISAR imaging methods are also presented based on the detection and motion parameters estimation results. Finally, both simulated and real-measured data are used to verify the remarkable detection, motion parameters estimation, and ISAR imaging performance compared with the traditional non-coherent accumulation and mixture of coherent and non-coherent accumulation methods.

A Robust Translational Motion Compensation Method Based on Weighted Optimization Framework—Second-Order Drift Particle Swarm Optimization

Accurate translational motion compensation is critical for ISAR imaging, especially for datasets with low signal-to-noise ratio (SNR) or sparse aperture. Among the existing translational motion compensation methods, the methods that based on the polynomial model have profound noise robustness but are easily affected by the variation of the reference distance. The methods that based on the range profile alignment have good versatility but are far from ideal under low SNR and sparse aperture environment. Therefore, we propose a novel and robust translational motion compensation method to overcome the shortcoming of the existing methods. Specifically, we estimate the translational phase error sequence by maximizing Laplacian average gray level and peak value. It can improve the robustness of translational motion compensation for noise and sparse aperture. Since such principle creates a large-scale multi-objective optimization problem, we design weighted optimization framework - second-order drift particle swarm optimization (WOF - SDPSO) to solve it accurately. WOF-SDPSO achieves full exploration of high-dimensional variable space by variable dimension reducing, random drift update, and second-order oscillation convergence. These ensure the global optimal solution easier to be found. Experimental results of the measured dataset prove that the proposed method is effective and accurate under the conditions of low SNR and sparse aperture. Moreover, the proposed method is noise robust for polynomial translational motion model with sparse aperture.

The Application of Machine Learning ICA-VMD in an Intelligent Diagnosis System in a Low SNR Environment.

This paper proposes a new method called independent component analysis–variational mode decomposition (ICA-VMD), which combines ICA and VMD. The purpose is to study the application of ICA-VMD in low signal-to-noise ratio (SNR) signal processing and data analysis. ICA is a very important method in the field of machine learning. It is an unsupervised learning algorithm that can dig out the independent factors hidden in the observation signal. The VMD method estimates each signal component by solving the frequency domain variational optimization problem, and it is very suitable for mechanical fault diagnosis. The advantage of ICA-VMD is that it requires two sensory cues to distinguish the original source from the unwanted noise. In the three cases studied here, the original source was first contaminated by white Gaussian noise. The three cases in this study are under different SNR conditions. The SNR in the first case is –6.46 dB, the SNR in the second case is –21.3728, and the SNR in the third case is –46.8177. The simulation results show that the ICA-VMD method can effectively recover the original source from the contaminated data. It is hoped that, in the future, there will be new discoveries and advances in science and technology to solve the noise interference problem through this method.

ISAR Imaging Motion Compensation in Low SNR Environments Using Phase Gradient and Filtering Techniques

In a low signal-to-noise ratio (SNR) environment, conventional range profiles-based motion compensation (MoCo) methods become inapplicable for inverse synthetic aperture radar (ISAR) imaging. In this article, a novel method for range profile alignment along with phase adjustment is proposed. The method is applied in the uncompressed phase history domain without the need for a prominent point. Motion errors are estimated from a phase map using only fast Fourier transform (FFT) and Hadamard multiplication operations, making the method suitable for real-time applications. The proposed method strives to aggregate the energy of all scatterers into a single peak by 2-D coherent accumulation incorporating the product of the radar pulse backscattering and its subsequent pulse, hence the reference to phase gradient processing (PGP). This process strengthens SNR and overcomes the problems associated with low signal power, even when the range profiles are submerged in noise. Experimental results are provided to demonstrate the performance of the proposed method compared with the state-of-the-art algorithms.

Speech endpoint detection in fixed differential beamforming combined with modulation domain

The actual speech signal is always in a complex environment, which affects the accuracy of speech endpoint detection. In order to improve the accuracy of speech endpoint detection under low Signal-To-Noise Ratio(SNR), a speech endpoint detection technology based on fixed differential beamforming and modulation domain is proposed. Firstly, two microphones of the first-order differential microphone arrays(DMAs) are used to collect the target speech and noise, and then a fixed differential beamformer is used to reduce the noise. Then, the phase compensated modulation domain spectral subtraction method is used to further eliminate the residual noise. Finally, the subband spectral entropy algorithm is used to detect the speech signal endpoint. The experimental results show that, compared with the existing methods, the proposed method has a higher accuracy of speech endpoint detection in the low SNR environment of -10∼5dB.