Low Signal-to-noise Ratio Environment Research Articles

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.

DOA Estimation in Low SNR Environment through Coprime Antenna Arrays: An Innovative Approach by Applying Flower Pollination Algorithm

The design of the modern computing paradigm of heuristics is an innovative development for parameter estimation of direction of arrival (DOA) using sparse antenna arrays. In this study, the optimization strength of the flower pollination algorithm (FPA) is exploited for the DOA estimation in a low signal to noise ratio (SNR) regime by applying coprime sensor arrays (CSA). The enhanced degree of freedom (DOF) is achieved with FPA by investigating the global minima of highly nonlinear cost function with multiple local minimas. The sparse structure of CSA demonstrates that the DOF up to O(MN) is achieved by employing M+N CSA elements, where M and N are the numbers of antenna elements used to construct the CSA. Performance analysis is conducted for estimation accuracy, robustness against noise, robustness against snapshots, frequency distribution of root mean square error (RMSE), variability analysis of RMSE, cumulative distribution function (CDF) of RMSE over Monte Carlo runs and the comparative studies of particle swarm optimization (PSO). These reveal the worth of the proposed methodology for estimating DOA.

Marine seismic signal denoising using VMD with Hausdorff distance and wavelet transform

In marine seismic acquisitions, signal interference remains a major menace. In this paper, a denoising approach based on the Variational Mode Decomposition (VMD) combined with the Hausdorff distance (HD) and Wavelet transform (WT) is proposed. There has been substantial research in this field over the years. However, traditional denoising methods fall short of achieving satisfactory results in an extremely low signal to noise ratio (SNR) environment. The feasibility, and stability of the proposed method was validated by performing simulations in MATLAB on both a synthetic signal and a seismic signal generated using real dataset. It was found that the proposed method does well in preserving marine signals in low SNR environments, and has a superior output SNR.

Microphone Array Speech Separation Algorithm Based on TC-ResNet

Traditional separation methods have limited ability to handle the speech separation problem in high reverberant and low signal-to-noise ratio (SNR) environments, and thus achieve unsatisfactory results. In this study, a convolutional neural network with temporal convolution and residual network (TC-ResNet) is proposed to realize speech separation in a complex acoustic environment. A simplified steered-response power phase transform, denoted as GSRP-PHAT, is employed to reduce the computational cost. The extracted features are reshaped to a special tensor as the system inputs and implements temporal convolution, which not only enlarges the receptive field of the convolution layer but also significantly reduces the network computational cost. Residual blocks are used to combine multiresolution features and accelerate the training procedure. A modified ideal ratio mask is applied as the training target. Simulation results demonstrate that the proposed microphone array speech separation algorithm based on TC-ResNet achieves a better performance in terms of distortion ratio, source-to-interference ratio, and short-time objective intelligibility in low SNR and high reverberant environments, particularly in untrained situations. This indicates that the proposed method has generalization to untrained conditions.

DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO

In general, the performance of a direction of arrival (DOA) estimator may decay under a non-uniform noise and low signal-to-noise ratio (SNR) environment. In this paper, a memetic particle swarm optimization (MPSO) algorithm combined with a noise variance estimator is proposed, in order to address this issue. The MPSO incorporates re-estimation of the noise variance and iterated local search algorithms into the particle swarm optimization (PSO) algorithm, resulting in higher efficiency and a reduction in non-uniform noise effects under a low SNR. The MPSO procedure is as follows: PSO is initially utilized to evaluate the signal DOA using a subspace maximum-likelihood (SML) method. Next, the best position of the swarm to estimate the noise variance is determined and the iterated local search algorithm to reduce the non-uniform noise effect is built. The proposed method uses the SML criterion to rebuild the noise variance for the iterated local search algorithm, in order to reduce non-uniform noise effects. Simulation experiments confirm that the DOA estimation methods are valid in a high SNR environment, but in a low SNR and non-uniform noise environment, the performance becomes poor because of the confusion between noise and signal sources. The proposed method incorporates the re-estimation of noise variance and an iterated local search algorithm in the PSO. This method is effectively improved by the ability to reduce estimation deviation in low SNR and non-uniform environments.

Robust Modified Multiple Signal Classification Algorithm for Direction of Arrival Estimation

A modified robust multiple signal classification (MUSIC) algorithm for direction-of-arrival (DOA) estimation of coherent source signals is devised in this work. Classical and subspace-based methods require large antenna elements and huge computations for the estimation of intended signals. It also deviates from its performance in low signal-to-noise ratio (SNR) conditions. Hence efficient and robust DOA estimators are required to tackle the real-time problems in wireless communication applications. To overcome these problems, the classical MUSIC algorithm is modified by assimilating Jordon canonical matrix in the covariance matrix for the reconstruction of data. With this modification, it is possible to make an accurate estimation of coherent sources even under an extremely low SNR environment. Furthermore, the proposed method requires fewer antenna elements, fewer snapshots, and less computation as compared to the classical MUSIC algorithm. The experimental results signify the effectiveness of the new method.

High-Resolution Radar Imaging in Low SNR Environments Based on Expectation Propagation

We address the problem of high-resolution radar imaging in low signal-to-noise ratio (SNR) environments in an approximate Bayesian inference framework. First, the probabilistic graphical model is constructed by imposing the sparsity-promoting spike-and-slab prior to the distribution of scattering centers. Then, the model parameters and phase errors are estimated iteratively by expectation propagation (EP) and maximum likelihood (ML) estimation. Compared with the available imaging methods based on the numerical optimization and Bayesian inference, the proposed method has exhibited more flexibility in data representation and better performance in parameter estimation, particularly in sparse-aperture and low SNR scenarios.

A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector

This paper presents a unified speech enhancement system to remove both background noise and interfering speech in serious noise environments by jointly utilizing the parabolic reflector model and neural beamformer. First, the amplification property of paraboloid is discussed, which significantly improves the Signal-to-Noise Ratio (SNR) of a desired signal. Therefore, an appropriate paraboloid channel is analyzed and designed through the boundary element method. On the other hand, a time-frequency masking approach and a mask-based beamforming approach are discussed and incorporated in an enhancement system. It is worth noticing that signals provided by the paraboloid and the beamformer are exactly complementary. Finally, these signals are employed in a learning-based fusion framework to further improve the system performance in low SNR environments. Experiments demonstrate that our system is effective and robust in five different noisy conditions (speech interfered with factory, pink, destroyer engine, volvo, and babble noise), as well as in different noise levels. Compared with the original noisy speech, significant average objective metrics improvements are about Δ STOI = 0.28, Δ PESQ = 1.31, Δ fwSegSNR = 11.9.

Decision-Driven Time-Adaptive Spectrum Sensing in Cognitive Radio Networks

In cognitive radio systems based on periodic sensing and transmission, a secondary user (SU) senses the activity of a primary user (PU) in the sensing phase and then makes use of the detected spectrum opportunity in the transmission phase. Under the constraint that the PU should be sufficiently protected, it is challenging for the SU to accurately sense and utilize spectrum opportunities in low signal to noise ratio (SNR) environments. However, in existing spectrum sensing schemes, the time-frequency resource blocks available for data transmissions are always wasted once a false alarm event happens in the sensing period where the SU is doing nothing but waiting for another sensing opportunity. By reutilizing these initially wasted time-frequency resource blocks for spectrum sensing, the SU can get more accurate information on the activities of the PU, which enables the SU to make more efficient use of the spectrum opportunities left by the PU. In this paper, a decision-driven time-adaptive spectrum sensing scheme is proposed to improve both spectral efficiency and energy efficiency based on improved resource usage. Specifically, if the PU is detected to be absent in the sensing phase of a frame, the SU transmits data in the transmission phase of the frame; otherwise, the SU will sense the spectrum for a duration of one frame period, where the frame period consists of the transmission phase of the current frame and the sensing phase of the next frame. When both signal and noise are Gaussian, the optimal maximum likelihood ratio detector is derived. Furthermore, the performance upper-bounds of spectrum utilization and secondary throughput are obtained. Finally, both simulation and theoretical results show that the developed scheme can improve spectrum utilization, secondary throughput and energy efficiency effectively in extremely low SNR environments.

LPI Radar Waveform Recognition Based on Features from Multiple Images.

Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is −6 dB.

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

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

Two-Step Thresholds TBD Algorithm for Time Sensitive Target Based on Dynamic Programming

Surveillance radar is confronted with serious two-dimensional ambiguity (i.e., range dimension and velocity dimension) and low signal-to-noise ratio (SNR) problems in the time sensitive (TS) target detection process, which may result in the deterioration of target detection performance. Combining the advantages of the intra-frame detection and the inter-frame detection, a novel two-step thresholds track before detect (TBD) algorithm based on the integration techniques, i.e., two-dimensional ambiguity resolution and multi-frame joint detection, is proposed in this paper. Firstly, the fusion design strategy, including the pulse repetition frequency, the carrier frequency and the orthogonal waveform, is adopted in the transmitting pulse sequence. As a result, not only the range ambiguity number and the spatial filling time are reduced synchronously, but the decoupling of range-velocity ambiguity is realized, which is conductive to multi-target detection in low SNR environment. Then, the first level adaptive threshold is obtained by employing the judgment criterion based on cumulative value function (CVF), and thus the false trajectory number is effectively eliminated from the perspective of cumulative amplitude. Finally, through analyzing the inherent relation between the local trajectory characteristics and the global trajectory characteristics comprehensively, different types of false alarm trajectories are further reduced by means of the polynomial coefficient variance statistics. Experimental results verify the effectiveness of the proposed algorithm.

An Efficient Range-Doppler Domain ISAR Imaging Approach for Rapidly Spinning Targets

Owing to the large range cell migration (RCM) and fast time-variant Doppler frequency modulation (DFM) generated by rapidly spinning targets, it is difficult to efficiently obtain well-focused inverse synthetic aperture radar (ISAR) images via conventional algorithms because of the multidimensional search requirement. Inspired by the inherent azimuth spatial invariance in strip-map synthetic aperture radar (SAR) imaging mode, an efficient range-Doppler domain ISAR imaging method for rapidly spinning targets is proposed in this article. First, echo signal is transformed into range-Doppler domain and its precise analytical expression is derived according to the principle of stationary phase (POSP). Second, the energy of scatterers distributed in different range cells is extracted along the rotating radius. By doing so, the energy is concentrated in the same range cell. After that, the high-order phase terms of the signal are compensated and the CLEAN technique is also applied to reduce the sidelobes of a strong scatterer. Finally, 3-D ISAR image of the spinning target is reconstructed by projecting the spatial parameters to 3-D cylindrical coordinates. Furthermore, in this article, the output signal-to-noise ratio (SNR) gain, anti-noise performance, the mismatched phase error, and the computational complexity analyses are also provided. Compared with existing approaches, the proposed method has advantages in the computational complexity and low SNR environment thanks to the only 1-D search and the coherent integration gain obtained. Both the theoretical derivations and the simulated results demonstrate the effectiveness of the proposed method.

An order-aware scheme for robust direction of arrival estimation in the spherical harmonic domain.

Direction of arrival (DOA) estimation of sound sources using a spherical microphone array is usually performed in the spherical harmonic (SH) domain. In a non-noisy environment, it suffices to use only the zeroth- and first-order spherical harmonic beams (SHBs) in the SH domain for DOA estimation. One such method is based on the pseudo-intensity vector (PIV), which is attractive due to its low computational complexity. To improve the performance of the PIV method in reverberant environments, some methods have been proposed recently to further exploit high-order SHBs. However, these methods ignore the effect of noise on high-order SHBs, which may lead to poor performance in low signal-to-noise ratio (SNR) environments. To address the problem, this paper proposes an order-aware scheme that is able to select the high-order SHBs reliable for robust DOA estimation of multiple speech sources. Simulation and real-world experimental results demonstrate that the order-aware scheme based methods outperform their existing counterparts with less computational complexity in terms of both accuracy and robustness of DOA estimation. Moreover, the performance improvement is more significant in low SNR environment and in a scenario with small angular separation of sources.

Emitter Signal Waveform Classification Based on Autocorrelation and Time-Frequency Analysis

Emitter signal waveform recognition and classification are necessary survival techniques in electronic warfare systems. The emitters use various techniques for power management and complex intra-pulse modulations, which can create what looks like a noisy signal to an intercept receiver, so emitter signal waveform recognition at a low signal-to-noise ratio (SNR) has gained increased attention. In this study, we propose an autocorrelation feature image construction technique (ACFICT) combined with a convolutional neural network (CNN) to maintain the unique feature of each signal, and a structure optimization for CNN input layer called hybrid model is designed to achieve image enhancement of the signal autocorrelation, which is different from using a single image combined with CNN to complete classification. We demonstrate the performance of ACFICT by comparing feature images generated by different signal pre-processing algorithms, and the evaluation indicators are signal recognition rate, image stability degree, and image restoration degree. This paper simulates six types of the signals by combining ACFICT with three types of hybrid model, the simulation results compared with the literature show that the proposed methods not only has a high universality, but also better adapts to waveform recognition at low SNR environment. When the SNR is –6 dB, the overall recognition rate of the method reaches 88%.

Identification and authentication for wireless transmission security based on RF-DNA fingerprint

For wireless transmission, radio-frequency device anti-cloning has become a major security issue. Radio-frequency distinct native attribute (RF-DNA) fingerprint is a developing technology to find the difference among RF devices and identify them. Comparing with previous research, (1) this paper proposed that mean (μ) feature should be added into RF-DNA fingerprint. Thus, totally four statistics (mean, standard deviation, skewness, and kurtosis) were calculated on instantaneous amplitude, phase, and frequency generated by Hilbert transform. (2) We first proposed using the logistic regression (LR) and support vector machine (SVM) to recognize such extracted fingerprint at different signal-to-noise ratio (SNR) environment. We compared their performance with traditional multiple discriminant analysis (MDA). (3) In addition, this paper also proposed to extract three sub-features (amplitude, phase, and frequency) separately to recognize extracted fingerprint under MDA. In order to make our results more universal, additive white Gaussian noise was adopted to simulate the real environment. The results show that (1) mean feature conducts an improvement in the classification accuracy, especially in low SNR environment. (2) MDA and SVM could successfully identify these RF devices, and the classification accuracy could reach 94%. Although the classification accuracy of LR is 89.2%, it could get the probability of each class. After adding a different noise, the recognition accuracy is more than 80% when SNR≥5 dB using MDA or SVM. (3) Frequency feature has more discriminant information. Phase and amplitude play an auxiliary but also pivotal role in classification recognition.

Wavelet Transform and Artificial Neural Network Based Spectrum Sensing in Cognitive Radio

With the progress and upgrade of wireless telecommunication technologies, the lack of spectrum resources is pretty serious. As a method to improve spectrum utilization, cognitive radio has received widespread attention. The technology of spectrum sensing, one of the most significant parts of cognitive radio, has important research value. The existing spectrum sensing technology is difficult to detect the main user signal at a lower signal-to-noise-ratio (SNR).To solve this problem, wavelet transform and artificial neural network based spectrum sensing in cognitive radio is put forward. The spectrum detection perceptual classifier is designed through the wavelet transform, cyclostationary feature and the artificial neural network. Firstly, the wavelet threshold method is used to process the received signal. The cyclic characteristic value of the processed signal is calculated. Secondly, the eigenvalues are learned and stored through the artificial neural network. Finally, the test data is brought into the trained network for classification. Verifying by simulation experiments, the proposed algorithm has better accuracy and effectiveness than the original method in the low SNR environment.

Radar Waveform Recognition Based on Multiple Autocorrelation Images

Radar signal waveform recognition, as a key component of radar target recognition, has always been a research topic of great concern in the field of electronic countermeasures. In this paper, aiming at the contradiction between improving recognition rate and reducing the sample size, we propose a multiple autocorrelation joint decision models, which achieves higher waveform recognition rate while requires lower data volume for the original sampled data. The key point of the model is to perform multiple autocorrelations on the signal and use time-frequency transform for each times autocorrelation result to obtain multiple time-frequency feature images that can characterize the same signal. Then, the model adapts to the input of multiple feature images and gets the pre-classification results of each feature image. Finally, using the pre-classification results, this paper designs an inference machine module based on a fully connected structure to achieve a better signal classification result. This paper simulates six types of the signals and generates training sets and test sets, using two data sets to achieve hyper parameter optimization, training, and testing of the model. The simulation results compared with the literature show that the proposed model not only has a high recognition rate at a high signal-to-noise ratio (SNR) but also better adapts to waveform recognition at low SNR environment. At −9dB SNR, the recognition rate of six types of signals is more than 74%.

A Finite Set Recognition Algorithm of LDPC Coding by Using Soft Decision Sequence

Aiming at Finite set identification problem of the channel encoding of LDPC code identification system, a novel coding identification algorithm for LDPC Codes using Soft decision sequence is proposed in this paper. The algorithm will introduce the relation of encoding check log-likelihood ratio(CLLR) to receive soft decision sequence. By analyzing statistical characteristics of CLLR, an identification algorithm using the MMSR decision will be proposed and then LDPC closed set identification will be completed. The algorithm comprehensively uses the statistical characteristics of the mean and variance of the data, and effectively avoids the defects of the traditional algorithm, when the identification effect is not ideal, under the low signal to noise ratio(SNR). From the simulation results, the algorithm is significantly better than the existing algorithm under the closed set application mode of a priori coded set, and the identification gain can reach 2 dB to 5dB in low SNR environment. Moreover, for the identification of high bit rate LDPC code, the identification effect of this algorithm is obviously better than the existing algorithm.