Signal Estimation Research Articles

Unwrapping error and fading signal correction on multi-looked InSAR data

Multi-looking, aimed at reducing data size and improving the signal-to-noise ratio, is indispensable for large-scale InSAR data processing. However, the resulting “Fading Signal” caused by multi-looking breaks the phase consistency among triplet interferograms and introduces bias into the estimated displacements. This inconsistency challenges the assumption that only unwrapping errors are involved in triplet phase closure. Therefore, untangling phase unwrapping errors and fading signals from triplet phase closure is critical to achieving more precise InSAR measurements. To address this challenge, we propose a new method that mitigates phase unwrapping errors and fading signals. This new method consists of two key steps. The first step is triplet phase closure-based stacking, which allows for the direct estimation of fading signals in each interferogram. The second step is Basis Pursuit Denoising-based unwrapping error correction, which transforms unwrapping error correction into sparse signal recovery. Through these two procedures, the new method can be seamlessly integrated into the traditional InSAR workflow. Additionally, the estimated fading signal can be directly used to derive soil moisture as a by-product of our method. Experimental results on the San Francisco Bay area demonstrate that the new method reduces velocity estimation errors by approximately 9 %–19 %, effectively addressing phase unwrapping errors and fading signals. This performance outperforms both ILP and Lasso methods, which only account for unwrapping errors in the triplet closure. Additionally, the derived by-product, soil moisture, shows strong consistency with most external soil moisture products.

Gridless DOA estimation of coherent signals based on Alternating Direction Multiplier Method

Gridless DOA estimation of coherent signals based on Alternating Direction Multiplier Method

A Comparison of Convolutional Neural Network Transfer Learning Regression Models for Remote Photoplethysmography Signal Estimation

This study explores the extraction of remote Photoplethysmography (rPPG) signals from images using various neural network architectures, addressing the challenge of accurate signal estimation in biomedical contexts. The objective is to evaluate the effectiveness of different models in capturing rPPG signals from dataset snapshots. Two training strategies were investigated: pre-training models with only the fully connected layer being fine-tuned and training the entire network from scratch. The analysis reveals that models trained from scratch consistently outperform their pre-trained counterparts in extracting rPPG signals. Among the architectures assessed, DenseNet121 demonstrated superior performance, offering the most reliable results in this context. These findings underscore the potential of neural networks in advancing rPPG signal extraction, which has promising applications in fields such as clinical monitoring and personalized medical care. This study contributes to the integration of advanced imaging techniques and neural network-based analysis in biomedical engineering, paving the way for more robust and efficient methodologies.

Evaluation of Glutathione T2 in the Human Brain Using J-Difference MRS at 3 T: Multicenter Multivendor Study.

The need to quantify brain glutathione (GSH) accurately by J-difference spectroscopy has stimulated assessment of the TE effects on GSH edited signals at the popular field strength 3 T. We performed multiple-TE J-difference MRS at two sites to evaluate the GSH T2 relaxation and TE dependence of the GSH signal resolution. Two 10-ms spectrally selective Gaussian editing RF pulses were implemented in 3 T MEGA-PRESS sequences at two sites having different vendors. The sequences were optimized, with numerical and phantom analyses, for editing of the GSH 2.95 ppm resonance. The timings of the editing pulses within the sequences were tailored for high-amplitude GSH signal production for a TE range of 58-160 ms. Invivo human brain data were collected at five TEs (58, 70, 88, 116, and 150 ms) from five subjects at each site. Following LCModel analysis of difference and edit-off spectra independently between the sites, metabolite T2 values were estimated with mono-exponential regression of the signal estimates. Simulations and phantom data indicated that the MEGA-edited GSH peak amplitude was progressively larger with increasing TE up to 125-140 ms and the maximum amplitude was 2- to 2.5-fold greater than the amplitude at TE of 58 ms. For invivo data, the edited GSH peak was the largest at TE of 88 ms among the five TEs. Brain GSH T2 was measured as 88 ± 11 ms from 10 subjects, with no significant difference between the sites. The LCModel-returned correlation coefficient between GSH and co-edited N-acetylaspartate (NAA) multiplet was significantly smaller at short TEs than at long TEs. Our data suggest that MEGA-edited GSH signal undergoes extensive attenuation with increasing TE due to the fast T2 relaxation, and the edited GSH signal can be well resolved at short TEs with small interferences from adjacent co-edited NAA multiplet.

Deep-Learning Techniques Applied for State-Variables Estimation of Two-Mass System

The article is focused on the application of neural models for state-variables estimation. The estimators are applied in the control structure (with the state speed controller) of the electric drive with an elastic shaft. The extended amount of feedback is an additional argument for the estimation of the signal. The calculations are performed for three deep neural structures based on the Convolutional Neural Network (CNN) and the long short-term memory (LSTM). The design stages and the overall concept in this case are completely different than with the applications of classical observers (e.g., the Luenberger, the Kalman filter) often used for similar objects. The direct identification of the mechanical part of the drive is not necessary. The parameters and the equations describing the plant are not used. Instead, the signals are used for training the neural networks. The results (performed for the nominal values of the two-mass system and presenting the robustness of the estimators) present the high precision of the signal estimation. The second part of the work deals with the hardware implementation of the neural estimators in the low-cost programmable device with the ARM core. The experimental transients confirm the features of the neural estimators noticed in the simulations.

A Deep Shrinkage Network for Direction-of-Arrival Estimation with Sparse Prior

Direction-of-arrival (DOA) estimation of underwater multipath signals plays a indispensable role in both military and civilian underwater applications. Despite its importance, accurately estimating DOA under multipath conditions is challenging due to the proximity of paths in the spatial domain. Current methods struggle with this problem in passive detection scenarios. To address these limitations, this study proposes a deep learning (DL)-based DOA estimation framework leveraging sparse representation. First, the approach models the array covariance matrix as an undersampled linear measurement of the spatial spectrum. Then, a super-resolution deep shrinkage reconstruction network (SDSR-Net) is designed to map the sparse representation of the covariance matrix directly to the DOA. The network integrates a shrinkage module as nonlinear transformation layers, promoting sparsity and enhancing the discrimination of features. Simulations and experimental evaluations validated the effectiveness of the proposed method, showing that the DOA estimation accuracy was significantly improved and able to achieve a resolution of 0.2° in the spatial spectrum. Compared with existing methods, SDSR-Net achieved superior performance by effectively utilizing a sparsity prior, maintaining a high-resolution performance at signal-to-noise ratios higher than −10 dB. This work contributes a robust and efficient solution to DOA estimation challenges in underwater environments.

Hydrological signatures in Brazilian catchments: a machine learning approach for spatial estimates

This study investigates the estimation of hydrological signatures in Brazilian catchments using physical attributes and machine learning algorithms. Using CABra’s dataset with 735 catchments, we tested 163 signatures with regression techniques, including Random Forest, Gradient Boosting, and Support Vector Regression (SVR). A key aspect was the algorithmic approach, providing models with topographic (area, elevation, slope), climatic (precipitation, evapotranspiration, aridity index), soil (silt, clay, organic content), and land cover attributes without assuming correlations. This enabled the models to uncover complex patterns. Cross-validation (k-fold) showed most signatures were satisfactorily predicted (R2 > 0.7), suggesting their application in ungauged basins. Climatic factors, like precipitation, were crucial for predicting most signatures, especially high-flow, while silt content and latitude influenced low-flow and baseflow. Slope and latitude were key for basin dynamics, influencing the flashiness index, and land cover was important for low-flow. CatBoost was the best model. The SHapley Additive exPlanation (SHAP) analysis highlighted the importance of variables with low linear correlation.

Accurate channel estimation of on-grid partially coherent compressive phase retrieval for mmWave massive MIMO systems

Abstract Channel estimation of sparse signal is a challenging task for millimeter-wave massive multi-input multi-output systems. Due to the hardware imperfections and use of large carrier frequency oscillator in hybrid precoding architectures leads to random phase drifts in the received pilots. Channel estimation with random phase drifts exploits partial coherence, where pilots share phase distortion within a frame but differ across different frames. To achieve the reliable and efficient communication, the support vector initialization needs to be optimized effectively. In this article, the singular value decomposition-based on-grid partially coherent compressive phase retrieval (SVD-PC-CPR) algorithm is proposed. This algorithm optimizes the initial support vector by finding the best dimensional subspace of the k sparsity elements by forming the singular vectors. The channel vectors are estimated by applying the gradient descent method through iterative refinements. This algorithm utilizes SVD for identifying the dominant paths from the phases of the measurements by selecting basis vectors. Based on the simulation results, the proposed algorithm achieves better normalized mean squared error, a multi-user sum-rate gain of 19 bits/s/Hz, and a 48 % improvement in estimation accuracy over the conventional PC-CPR algorithm at 20 dB signal-to-noise ratio.

Joint Signature Estimation and Angular Domain Channel Reuse Based Random Access for Massive Connectivity

Joint Signature Estimation and Angular Domain Channel Reuse Based Random Access for Massive Connectivity

A Self-Adaptive Frequency Decomposition Approach for Denoising to Enhance Data-Driven Learning of Cyclic Time Series in Medical Signal Estimation

A Self-Adaptive Frequency Decomposition Approach for Denoising to Enhance Data-Driven Learning of Cyclic Time Series in Medical Signal Estimation

Correction to: Householder Transform based Estimation of Signal and Sparsifying Basis from Blind Compressive Measurements

Correction to: Householder Transform based Estimation of Signal and Sparsifying Basis from Blind Compressive Measurements

Estimation of single-beam echo sounder signal for riverbed classification

Estimation of single-beam echo sounder signal for riverbed classification

A High-Precision DOA Estimator for Low-Frequency Signals Using Ultra-Dense Small-Aperture Microphone Arrays

Abstract Accurate Direction of Arrival (DOA) estimation is critical for the effectiveness of Unattended Ground Sensor (UGS) systems, as it enhances sound localization, situational awareness, resource optimization, and facilitates integration with other sensor data for comprehensive monitoring. With the growing demand for lightweight and miniaturized sensors suitable for diverse environments, challenges arise in DOA estimation of low-frequency signals using dense small-aperture microphone arrays, especially under noisy conditions. Despite advancements in deep learning, both conventional and existing neural network methods struggle with this task. In this paper, we present the Multi-Resblock DOA Network (MRDNet), a novel neural network designed for precise DOA estimation of low-frequency sounds in noisy environments using small-aperture arrays. MRDNet was evaluated under simulations involving Brownian and Gaussian noise, representing wind and general background disturbances. The results demonstrate that MRDNet achieves superior accuracy, with a Mean Angular Error (MAE) of 2.797 degrees, significantly outperforming baseline methods by 24.93\%. Furthermore, we show that increasing the number of microphones within a constant array size using MRDNet effectively enhances DOA accuracy in the context of deep learning.

Fast gridless direction-of-arrival estimation for wideband linear frequency modulated signals Based on fractional Fourier transform

Fast gridless direction-of-arrival estimation for wideband linear frequency modulated signals Based on fractional Fourier transform

Broadening the application of angle-time cyclostationary analysis in bearing fault diagnosis using precise speed reconstruction

Angle-time cyclostationary (AT-CS) analysis has developed into the most powerful analytical tool for studying vibration signals of rotating machinery under variable operating conditions. However, due to limitations in the accuracy of traditional rotational speed estimation algorithms, the effectiveness of AT-CS analysis may be compromised in the absence of robust keyphasor signal. To address this challenge, a novel rotational speed signal estimation method based on “double estimation” strategy is proposed to expand the application scenarios of AT-CS. This methodology entails an initial estimation of the rough speed through time-frequency analysis, followed by refinement using phase demodulation techniques. Additionally, the Vold–Kalman order tracking method is employed to isolate the single frequency signal component essential for phase demodulation techniques. In situations where the spectrum is complex and the speed fluctuates violently, this method can still accurately obtain the speed signal, achieving similar effects to the rotational velocity transducer. The complex speed curve experiment and turbine pump bearing experiment with severe speed fluctuation are carried out to verify the algorithm.

A rapid multi-modal parameter estimation technique for LISA

Abstract The laser interferometer space antenna (LISA) will observe gravitational-wave (GW) signals from a wide range of sources, including massive black hole binaries (MBHBs). Although numerous techniques have been developed to perform Bayesian inference for LISA, they are often computationally expensive; analyses often take at least ∼1 month on a single CPU, even when using accelerated techniques. Not only does this make it difficult to concurrently analyse more than one GW signal, it also makes it challenging to rapidly produce parameter estimates for possible electromagnetic follow-up campaigns. simple-pe was recently developed to produce rapid parameter estimates for GW signals observed with ground-based GW detectors. In this work, we extend simple-pe to produce rapid parameter estimates for LISA sources, including the effects of higher order multipole moments. We show that simple-pe infers the source properties of massive black hole binaries in zero-noise at least ∼ 100 × faster than existing techniques; ∼ 12 h on a single CPU. We further demonstrate that simple-pe can be applied before existing Bayesian techniques to mitigate biases in multi-modal parameter estimation analyses of MBHBs.

Fast DOA Estimation of IGS Noncircular Signals Under Coprime Arrays

Traditional direction-of-arrival (DOA) estimation methods often encounter challenges such as low accuracy and high computational complexity, particularly in complex signal environments with multiple sources. To address these issues, this paper proposes a fast DOA estimation method for improper Gaussian signaling (IGS) non-circular signals, based on a coprime array; this approach involves extending the array model by leveraging the unique properties of non-circular signals. Subsequently, this study introduces the ANCM algorithm, a method designed for fast DOA estimation of IGS non-circular signals within a non-uniform noise background. The proposed ANCM algorithm is then compared to classical algorithms, through simulations in a non-uniform noise environment. Experimental results demonstrate that the ANCM algorithm significantly outperforms traditional methods in such conditions. Therefore, the development of new, efficient DOA estimation methods holds substantial theoretical and practical value.

Efficient Nonparametric Estimation of 3D Point Cloud Signals through Distributed Learning

Advancements in technology have elevated the prominence of 3D point cloud data, making its analysis increasingly vital across various applications. This need drives the demand for advanced statistical analytic approaches to handle challenges such as size, sparsity, and irregularity in 3D point clouds while ensuring accurate and efficient information extraction. This article introduces a novel nonparametric distributed (NPD) learning framework that uses trivariate spline smoothing over a triangulation of domain. The proposed NPD algorithm features a straightforward, scalable, and communication-efficient implementation scheme that can achieve near-linear speedup. In addition, we provide rigorous theoretical support for the NPD estimation framework and demonstrate that the NPD spline estimators attain the same convergence rate as the global spline estimators obtained using the entire dataset and achieve the optimal nonparametric convergence rate established by Stone under some regularity conditions. To evaluate the efficacy of the proposed NPD method, we conduct simulation studies comparing it with several global nonparametric estimation methods used to smooth the 3D data. The results demonstrate the superior performance of the NPD method in accurately and efficiently processing and learning from 3D point clouds, highlighting its potential to advance large and complex data analysis. Supplementary material, which contains related technical details, proofs of the theoretical results, and additional results in simulation studies, is available online.

Suppressing data anomalies of gravitational reference sensors with time delay interferometry combinations

For the LISA and Taiji missions, both transient and continuous data anomalies would pose significant challenges to the detection, estimation, and subsequent scientific interpretation of gravitational wave signals. As is indicated by the experiences of LISA PathFinder and Taiji-1, these anomalies may originate from the disturbances of the gravitational reference sensors due to routine maintenances and unexpected environmental or instrumental issues. To effectively mitigate such anomalies and thereby enhance the robustness and reliability of the scientific outputs, we suggest to employ the “position noise suppressing” time delay interferometry channels. Through analytical derivations and numerical simulations, we demonstrate that these time delay interferometry channels can suppress data anomalies by more than 2 orders of magnitude within the sensitive band of 0.1 mHz - 0.05 Hz, while still remaining sensitive to most of the target signals. Compared with existing research that focuses on reconstructing and subtracting data anomalies, our method does not rely on the prior knowledge about the models of anomalies. Furthermore, the potential application scenarios of these channels have also been explored.

Abstract 4138407: Exome-wide association study of over a million individuals identified effector genes and variants for blood lipids

Background: Inherited hypercholesterolemia is an important Mendelian condition associated with early onset myocardial infarction. However, current genetic diagnostic criteria only partially explain severe clinical hypercholesterolemia. Furthermore, current pathogenicity categorizations do not consistently inform prognosis, but quantification is limited by scarcity of such rare variants. Aim: Exploiting populations scale genotype/phenotype data, to determine the architecture of the genetic variants associating with blood lipid levels with quantitative assessments. Methods: Here, we discovered and characterized rare coding alleles contributing to genetic dyslipidemia, a principal risk for coronary artery disease, among over a million individuals combining two large contemporary genetic datasets (Million Veteran Program N = 626,554 and UK Biobank, N = 431,026). Results: Testing 2,869,269 rare (minor allele frequency < 1%), coding variants, we identified 793 exome-wide significant associations ( P < 4.4 × 10 -11 ), including 8 previously undiscovered loci. Associated alleles are highly enriched in functional variant classes; high confidence predicted loss of function, deleterious missense, and cryptic splicing, showed significant and recessive associations, had similar effects across populations. Overall, rare variants contributed to additional > 16% of genetic trait variances on top of common genetic variants. The effect estimates of associated signals confirmed and refined curated pathogenic database, highlighted by the identification of African or South Asian specific pathogenic alleles previously controversial in its pathogenicity. Conclusion: This study provides resources and insights for understanding causal mechanisms and quantifying the expressivity of rare coding alleles for blood lipids by large scale sequencing study. Furthermore, identifying and characterizing population enriched variants, our study underscored the importance of multi-population rare variant study.