A Novel Speed Compensation Method for ISAR Imaging with Low SNR.

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

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.

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.

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.

This paper presents an algorithm for estimating the parameters of multicomponent chirp signals. The estimator is based on the cubic phase function (CPF), which is efficient to estimate the parameters of monocomponent polynomial phase signals (PPS) with order is less than or equal to 3. When the CPF is dealing with multicomponent chirp signals, the spurious peaks arise and thus the identifiability problem occurs. A new approach based on the transformation called product cubic phase function (PCPF) is proposed to remove this problem. This estimator offers a number of advantages with respect to CPF including improved noise rejection, suppression of cross terms, and elimination of spurious peaks. The algorithm is verified by simulation results

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.

Inverse synthetic aperture radar (ISAR) imaging is one of the important high- resolution radar applications for moving target detection and identification. The radial velocity, radial acceleration, and acceleration rate of maneuvering targets can cause range cell migration and Doppler variation in the received signals, which will degrade the imaging quality. Therefore, these parameters should be estimated and compensated before imaging. In low signal-to-noise ratio (SNR) conditions, the conventional motion parameter estimation and compensation algorithms for maneuvering targets do not work properly. To effectively detect and identify the maneuvering targets in low SNR conditions, a new radar imaging algorithm is proposed. The major contributions are as follows: 1) a novel algorithm for envelope migration correction is proposed with adjacent cross correlation function; 2) the target's motion parameters are estimated with the operations of Keystone transform and time-frequency transform to the adjacent cross correlation; 3) a theoretical analysis of the impact of motion parameters on the envelope migration is made, then cross-range phase estimation with the adjacent cross function is presented; 4) based on the estimated motion parameters of targets, a new algorithm for high-resolution ISAR imaging is developed.

We focus on the sound source localization (SSL) problem of small/weak voices recorded by small humanoid robots, such as Nao. The localization of small voice on such robots is more difficult than those with large bodies because 1) the number of microphones and their positions are restricted and 2) the signal to noise ratio (SNR) between internal noise and human speech is low. Multiple Signal Classification (MUSIC) is a promising noise-robust SSL method, and it has several variants in its eigenvector decomposition process, such as generalized eigenvalue decomposition (GEVD-MUSIC) and generalized singular value decomposition (GSVD-MUSIC). However, their performances are seriously affected by noise properties, the number of microphone, and their configurations on each robot's body. Since these have been confirmed only on robots with large bodies and many microphones, we need to investigate which variants perform better on a small humanoid robot with internal noise in it. We furthermore propose another MUSIC based on transformed steering vector (TSV-MUSIC) as a new implementation of GEVD-MUSIC. The computational cost of TSV-MUSIC is reduced compared with GEVD-MUSIC by changing its matrix multiplication procedures. Experimental results using real recoded data showed that our TSV-MUSIC outperformed others in terms of localization correctness by about 10 points under low SNR condition. We also compared the properties and performances of MUSIC-based SSLs by using simulated data.

Non-uniform sampled cubic phase function

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.

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.

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.

This paper investigates the integration of coded caching (CC) into fluid antenna (FA) multiple access (FAMA) systems to overcome their fundamental performance limitations. While FAMA has demonstrated strong interference suppression capabilities without relying on precoding, its delivery rate saturates in the high signal-to-noise ratio (SNR) regime due to residual multi-user interference, and it requires at least as many transmit antennas as served users. On the other hand, CC eliminates multi-user interference by jointly designing caching and delivery phases, but suffers from the well-known worst-user bottleneck, especially under wireless fading channels and low SNR conditions. To overcome the interference-limited nature of FAMA and the worst-user bottleneck in CC, we consider a CC-enabled FAMA framework. The proposed approach enables interference-free transmission to multiple users using a single transmit antenna, and leverages adaptive port selection at the users to combat channel fading. We analyze the average rates and effective gains of XOR-based CC and the recently developed aggregated CC (ACC) schemes under the FAMA framework, and derive simple closed-form approximations that accurately characterize the performance. Furthermore, we rigorously prove that, in the limit where the number of FA ports grows without bound while maintaining sufficient spatial diversity, the effective multiplexing gains in the low-SNR limit of both XCC- and ACC-enabled FAMA asymptotically achieve the nominal gain that is only attainable under high-SNR conditions with traditional antennas. Simulation results show that the proposed architecture outperforms both conventional FAMA and traditional-antenna CC schemes, achieving significant spectral efficiency gains in a wide range of SNR regimes.

The identifiability problem of the cubic phase function (CPF) for multicomponent quadratic FM (QFM) signals is verified both in theory and in simulations. A computationally efficient technique based on the one dimensional (1-D) Radon-CPF transform (RCT) is proposed for this problem. The RCT first reassigns the time-frequency rate distribution (TFRD) with the information of the second-order coefficient, and then implements the 1-D Radon transform with only angle variety. Although this technique is supposed for multicomponent QFM signals, it is also efficient for monocomponent. The simulation results verify the proposed method both in illustrative examples and performance evaluations.

Generally, the performance of endpoint detection is affected by the noise. In this paper, we propose a novel two-layer decision model based on noise classification to detect the activity voice robustly. The training processing mainly contains two steps: firstly, we employ the noisex-92 database, which consists of different types of pure noise, to train a BP neural network in order to classify the noise type precisely, secondly, we train BP neural networks for each noise type covering large range of signal noise ratio (SNR). In the testing phase, we assume that the short period of silence at the beginning of the signal contains features for noise and utilize them to get the noise type. Then, we use the classifier corresponding to the noise type to detect activity voice. We conduct experiments on TIMIT corpus for 5 noise types under 7 SNR conditions. And experimental results show that our method outperforms global classifier, especially in low SNR condition.