Underwater Coherent Source Direction-of-Arrival Estimation Method Based on PGR-SubspaceNet

Acoustic bird species classification under low SNR and small-scale dataset conditions

In the last few years, the demand of robust beamforming techniques has been increased significantly to serve numerous purposes. However, beamforming under low Signal to Noise Ratio (SNR) condition has always been a challenging task, which becomes more tedious in case of multiple sources. On the other hand, there are numerous factors such as steering vector error or similar estimation error problems that confine the efficiency of major beamforming techniques. Classical methods employ Eigen-Value Decomposition (EVD) and Singular Value Decomposition (SVD) over covariance matrix of the received signal; however their efficiency is limited under low SNR and multi-source condition, and perform inferiorly in case of steering vector errors. Such approaches are limited to achieve optimal Direction-of-Arrival (DOA) estimation that degrades the overall performance of the beamformer. Considering all these limitations, in this research, the focus is made on designing a robust DOA estimation model, followed by a novel beamformer for 8-element Uniform Linear Array (ULA). The simulation work consists two parts DOA estimation and beamforming. Unlike classical MUSIC and ESPRIT algorithms popular for DOA estimation our proposed model exploits the propagation method based subspace method where at first covariance matrix is obtained for the received signal, which is followed by normalization and Cross-Correlation matrix estimation. The obtained correlation matrix is processed for Generalized Eigen-Value Decomposition (GEVD) that efficiently obtained (orthogonal) subspace and SOI separation to yield DOA information. This approach enables more efficient DOA estimation even under low-SNR and steering vector error conditions. After obtaining DOA information, a novel adaptive beamformer is designed by using MVDR and FROST in parallel. DOA information from proposed model is given as input to the adaptive MVDR FROST beamforming model. Exploiting dynamic Signal-to-Interference ratio (SINR) by each beamformer our proposed method introduces an adaptation model that intends to achieve higher SINR value. MATLAB 2018a based simulation confirmed the robustness of the proposed model in terms of DOA estimation and beamforming results.

P174 ATTENUATING MULTIPLES FOR LOW SIGNAL- TO-NOISE RATIO DATA USING OPTIMAL BEAMFORMING 1 Abstract This paper presents the theory of optimal beamforming and its application in attenuating multiples for low signalto-noise (S/N) ratio data. The key point of the theory is updating the constraint in optimal beamformer design by using adaptive beamformer with the quiescent weighted vector. Real data example shows that the newly developed optimal beamformer has improved the minimum variance unbiased (MVU) beamformer significantly in attenuating multiples for low folding and/or low S/N ratio data without much extra cost. Introduction The objective of seismic data processing is to

This study proposes a bi-directional recurrent neural network (Bi-RNN) post-processing method for speech enhancement (SE) at low signal-to noise ratios (SNR). Current speech enhancement solutions performed badly under low SNR situations. Loizou and Kim proposed a solution to reduce speech distortion errors in time-frequency (T-F) domain but it requires the knowledge of ground truth. As ground truth is unknown in real-life applications, the current study proposes to use a Bi-RNN to implement Loizou and Kim’s solution as a post-processing method for SE engines. Our solutions do not require prior knowledge of ground truth. The effectiveness of the proposed method is investigated with a spectral subtraction (SS) SE engine, a non-negative matrix factorization (NMF) SE engine, and a deep neural network ideal ratio mask (DNN-IRM) SE engine, under matched/mis-matched noise and different SNR conditions. Experimental results demonstrate that the proposed post-processing method effectively improved both perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI) for all of these SE engines, especially at low SNR conditions.

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.

Grid-less DOA estimation of coherent sources based on the covariance matrix recovery

In this paper, a robust multiple observation likelihood ratio test (MOLRT) based voice activity detection (VAD) method is proposed. At the beginning of this paper, we introduce the Wiener filter to the observed signal in time domain which can help mitigating some noise. The reason why we use the Wiener filter for VAD is that the performance of the VAD is always better in high signal to noise ratio (SNR) range than in low SNR range. Then, some ideas are proposed to improve the performance of the MOLRT based VAD method. As we all know, a conventional MOLRT based method including three modules named likelihood ratio (LR) estimation, threshold setting and hangover technique. To improve the estimation accuracy of LR, we adopt the unbiased minimum mean-square error (MMSE) algorithm for noise power spectrum estimation in every frame, which is very effective for LR estimation in MOLRT-based VAD method. That is because the LR is a function of a prior and a posterior SNR and unbiased MMSE algorithm is very useful for noise estimation. In addition, to make our VAD method more robust, a dynamic threshold setting technique is proposed in our method, which is related to the minimum noise power spectrum. That is because minimum noise power spectrum can help us updating the value of threshold to a suitable level according to the denoised signal. Last but most important, a novel hangover algorithm is introduced in this paper comparing to the conventional HMM based hangover algorithm. In the novel hangover algorithm, the current frame is determined by the statistical result of the following speech/non-speech detections based on the likelihood ratio test. And the evaluation results reveal that proposed method significantly outperform the baseline result of LRT as regards VAD accuracy in both noise variations and low SNR conditions.

The acoustic pyrometer system uses physical properties of the gas and registers the perceivable temperature without the effects of radiation, and the measuring accuracy of acoustic wave flight time is the major factor in its application. The traditional correlation algorithm could not overcome convolution interference and calculate the time delay of acoustic signals accurately, under the low SNR (Signal Noise Ratio) and complex reverberation noise conditions. This paper presents a generalized cross power spectrum algorithm to filter the product and convolution interferences, and improves the performances of anti-noise and anti-convolution by whitening the sample signals and adjusting the weighted value of cross power spectrum algorithm with the variation of SNR. The experimental result and theoretical analysis showed that the new generalized cross power spectrum algorithm compared to the traditional correlation function analytical algorithm, can overcome the convolution interferences from reverberation noise, and sharpen peak value, thereby estimate the time delays of signals accurately.

Beamforming technique based on adaptive diagonal loading in wireless access networks

Online extraction of physical parameters of photovoltaic modules in a building-integrated photovoltaic system

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–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.

ABSTRACTThe problem of estimating the direction of arrival (DoA) of multiple far-field moving targets, producing broad-banded chirp signals, in both active and passive mode of operation is addressed. As the chirp signal, commonly used in both radar and sonar systems is better processed in fractional Fourier domain, the detection is done using fractional Fourier transform (FrFT) in the proposed work. Subsequently, the DoA estimation is achieved using both conventional and subspace methods. Even though FrFT beam-forming method has been studied in the past, its performance in respect of varying signal-to-noise ratios (SNRs) and multiple target detection has not been extensively analyzed yet. The results of simulation experiments reported in the present work show that FrFT enjoys better estimation accuracy over the conventional frequency domain beam-forming for chirp signals using Fourier transform (FT), in terms of computational efficiency, accuracy and resolution with low SNR, limited snapshots and sensors, and spatially coherent/multi-path signals. The performance metrics used for the study are (i) root mean square error, (ii) 3-dB beam width and (iii) CPU time for accuracy, resolution and computation time, respectively. It is seen from computer simulations that MUSIC outperforms other DoA estimation techniques from the performance curves for both active and passive systems.

Low signal-to-noise ratio (SNR) low probability of intercept (LPI) radar signal recognition is a key technical challenge in the field of electronic reconnaissance. Under low SNR conditions, severe noise contamination hinders the extraction of time-frequency features, resulting in degraded performance for traditional recognition methods. To address this issue, this paper proposes a cascaded framework, named MAE-WGAN, which integrates a Multi-Attention Enhanced Wasserstein Generative Adversarial Network for time-frequency image denoising, followed by radar signal recognition using a Convolutional Neural Network (CNN). The proposed approach embeds channel, spatial, and self-attention mechanisms into the U-Net-based generator of WGAN, forming a multi-stage radar signal processing system. First, under the guidance of high-quality time-frequency images, MAE-WGAN adaptively learns key time-frequency features using the multi-attention modules. Next, a discriminator is trained within the WGAN framework to recover signal structures obscured by noise. Finally, a dedicated multi-level feature extraction network is employed for accurate radar signal classification. Experimental results on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2}$</tex> typical LPI radar signals demonstrate that MAE-WGAN achieves high recognition accuracy under low SNR conditions, exhibiting strong recognition performance and robustness across varying SNR conditions.

In this paper, two novel speed compensation algorithms for ISAR imaging under a low signal-to-noise ratio (SNR) condition have been proposed, which are based on the cubic phase function (CPF) and the integrated cubic phase function (ICPF), respectively. These two algorithms can estimate the speed of the target from the wideband radar echo directly, which breaks the limitation of speed measuring in a radar system. With the utilization of non-coherent accumulation, the ICPF-based speed compensation algorithm is robust to noise and can meet the requirement of speed compensation for ISAR imaging under a low SNR condition. Moreover, a fast searching implementation strategy, which consists of coarse search and precise search, has been introduced to decrease the computational burden of speed compensation based on CPF and ICPF. Experimental results based on radar data validate the effectiveness of the proposed algorithms.

Signal time-frequency analysis under low signal-to-noise ratio (SNR) conditions is an important part of signal processing. As Wigner-Ville distribution (WVD) transformation can save all the traits of the original signal in the special space where the frequency is zero, a new method is proposed to extract the signal time-frequency relationship under low SNR conditions and to estimate the extent to improve the SNR after the denoising processing. Through signal time-frequency extracting comparison and parameter extracting comparison, it is proved that this method has higher extraction validity and feasibility.