Signal Estimation Research Articles

Spectral Technique for Baud Time Estimation

A new approach for baud time (or baud rate) estimation of a random binary signal is presented. This approach utilizes the spectrum of the signal after nonlinear processing in a way that the estimation error can be reduced by simply increasing the number of the processed samples instead of increasing the sampling rate. The spectrum of the new signal is shown to give an accurate estimate about the baud time when there is no apriory information or any restricting preassumptions. The performance of the estimator for random binary square waves perturbed by white Gaussian noise and ISI is evaluated and compared with that of the conventional estimator of the zero crossing detector.

An extremum-guided interpolation for sparsely sampled photoacoustic imaging

In photoacoustic (PA) reconstruction, spatial constraints or real-time system requirements often result to sparse PA sampling data. For sparse PA sensor data, the sparse spatial and dense temporal sampling often leads to poor signal continuity. To address the structural characteristics of sparse PA signals, a data interpolation algorithm based on extremum-guided interpolation is proposed. This algorithm is based on the continuity of the signal, and can complete the estimation of high sampling rate signals without complex mathematical calculations. PA signal data is interpolated and reconstructed, and the results are evaluated using image quality assessment methods. The simulation and experimental results show that the proposed method performs better than several typical algorithms, effectively restoring image details, suppressing the generation of artifacts and noise, and improving the quality of PA reconstruction under sparse sampling.

Seismic time-frequency masking for suppression of seismic speckle noise

Seismic speckle noise is the primary factor causing severe reflection distortions caused by small-scale near-surface scattering. As in the case of speckle noise in optics and acoustics, deterministic velocity model-building techniques cannot recover these heterogeneities which are much smaller than a wavelength. Conventional processing techniques struggle to perform when multiplicative noise remains unsuppressed. Although local and global stacking mitigates the effects of speckle noise, it leads to a severe loss of higher frequencies reducing the vertical resolution of the seismic data. The foundation for attacking speckle noise is a recently proposed mathematical model that includes two concurrent random multiplicative noise types: type 1 defining residual statics and type 2 describing random frequency-dependent phase perturbations that mimic small-scale near-surface scattering. Using this model, we have developed seismic time-frequency masking to suppress speckle noise on prestack data. The time-dependent and non-surface-consistent nature of scattering noise dictates window-based approaches borrowed from local stacking techniques. Separate manipulation of phase and amplitude spectra is achieved through modified time-frequency masking inspired by speech processing. A beamformed data set from local stacking is used as a guide for designing phase and amplitude masks that are directly applied to raw data in the time-frequency domain. The phase mask allows the restoration of coherency by repairing phase distortions caused by near-surface scattering. The amplitude mask corrects power spectrum (PS) distortions caused by multiplicative and additive noises. An amplitude mask is implemented using a data-driven minimum statistic approach that estimates the noise PS in each local window. The minimum statistics approach is adapted from speech processing using beamformed data as an initial signal estimate and as a guide to designing an improved amplitude mask. Synthetic and field data examples suggest significant improvements in coherency and signal-to-noise ratio after the suppression of multiplicative noise, which makes the data processable by conventional techniques subsequently.

Wideband DOA Estimation Utilizing a Hierarchical Prior Based on Variational Bayesian Inference

The direction-of-arrival (DOA) estimation of wideband signals, based on sparse signal reconstruction, has recently been proposed, owing to its unique high-resolution performance. As a typical tool of sparse signal reconstruction, sparse Bayesian learning (SBL) enhances little sparsity in most works, leading to a non-robust local fitting. To significantly enhance sparsity, we proposed a novel hierarchical Bayesian prior framework, and deduced a novel iterative approach. It was discovered that the iterative approach had a lower computational complexity than the majority of current state-of-the-art algorithms. Besides, the proposed approach achieves a high angular estimation accuracy and sparsity performance, by utilizing the joint sparsity of the multiple measurement vector (MMV) models. Moreover, the approach stabilizes the estimated values between different frequencies or snapshots, so as to obtain a flat spatial spectrum. Extensive simulation results are presented, to demonstrate the superior performance of our method.

Low-Complexity Joint Angle of Arrival and Time of Arrival Estimation of Multipath Signal in UWB System.

In an ultra-wideband (UWB) system, the two-dimensional (2D) multiple signal classification (MUSIC) algorithms based on high-precision 2D spectral peak search can jointly estimate the time of arrival (TOA) and angle of arrival (AOA). However, the computational complexity of 2D-MUSIC is very high, and the corresponding data model is only based on the dual antennas. To solve these problems, a low-complexity algorithm for joint AOA and TOA estimation of the multipath ultra-wideband signal is proposed. Firstly, the dual antenna sensing data model is extended to the antenna array case. Then, based on the array-sensing data model, the proposed algorithm transforms the 2D spectral peak search of 2D-MUSIC into a secondary optimization problem to extract the estimation of AOA via only 1D search. Finally, the acquired AOA estimations are brought back, and the TOA estimations are also obtained through a 1D search. Moreover, in the case of an unknown transmitted signal waveform, the proposed method can still distinguish the main path signal based on the time difference of arrival of different paths, which shows wider applications. The simulation results show that the proposed algorithm outperforms the Root-MUSIC algorithm and the estimation of signal parameters using the rotational invariance techniques (ESPRIT) algorithm, and keeps the same estimation accuracy but with greatly reduced computational complexity compared to the 2D-MUSIC algorithm.

A novel brain source reconstruction using a multivariate mode decomposition

Objective. Brain source reconstruction through electroencephalogram is a challenging issue in brain research with possible applications in cognitive science as well as brain damage and dysfunction recognition. Its goal is to estimate the location of each source in the brain along with the signal being produced. Approach. In this paper, by assuming a small number of band limited sources, we propose a novel method for the problem by using successive multivariate variational mode decomposition (SMVMD). Our new method can be considered as a blind source estimation method, which means that it is capable of extracting the source signal without the knowledge of the location of the source or its lead field vector. In addition, the source location can be determined through comparing the mixing vector found in SMVMD and the lead filed vectors of the entire brain. Main results. The simulations verify that our method leads to performance improvement in comparison to the well-known localization and source signal estimation techniques such as MUltiple SIgnal Calssification (MUSIC), recursively applied and projected MUSIC, dipole fitting method, MV beamformer, and standardized low-resolution brain electromagnetic tomography. Significance. The proposed method enjoys low computational complexity. Moreover, our investigations on some experimental epileptic data confirm its superiority over the MUSIC method in the aspect of localization accuracy.

Direction‐of‐arrival estimation for wideband linear frequency modulated signals with weighted l1‐norm in the fractional Fourier domain

AbstractDirection‐of‐arrival (DOA) estimation methods for wideband linear frequency modulated (LFM) signals based on fractional Fourier transform (FRFT) typically use the single snapshot vector in the fractional Fourier (FRF) domain. However, the low‐rank problem of the second‐order statistics in the single snapshot vector can decrease the DOA estimation resolution and cause issues in DOA estimation for coherent sources. In order to address these problems, a weighted l1‐norm method based on FRFT is proposed. The proposed method utilises the energy aggregation property of FRFT for LFM signals. This allows for obtaining a single snapshot vector in the FRF domain. Subsequently, a sparse representation DOA estimation model is established by taking advantage of the sparsity of the target distribution in the spatial domain. The DOA estimation is then obtained by minimising the l1‐norm. To further enhance the DOA estimation performance, we construct a matrix by rearranging the elements of the eigenvectors corresponding to the nonzero eigenvalue of the covariance matrix in the FRF domain. The rank of this matrix is equal to the number of sources, ensuring the orthogonality of the subspace obtained by the reconstructed matrix. Finally, a weighted matrix is constructed by exploiting the subspace orthogonality principle to adjust the sparsity penalty of the l1 constraint model. Simulation results demonstrate that our method is capable of achieving high angular resolution and accuracy even at low SNR and can process coherent signals directly.

Next-Generation URLLC With Massive Devices: A Unified Semi-Blind Detection Framework for Sourced and Unsourced Random Access

This paper proposes a unified semi-blind detection framework for sourced and unsourced random access (RA), which enables next-generation ultra-reliable low-latency communications (URLLC) with a massive number of devices. Specifically, the active devices transmit their uplink access signals in a grant-free manner to realize ultra-low access latency. Meanwhile, the base station aims to achieve ultra-reliable data detection under severe inter-device interference without exploiting explicit channel state information (CSI). We first propose an efficient transmitter design, where a small amount of reference information (RI) is embedded in the access signal to resolve the inherent ambiguities incurred by the unknown CSI. At the receiver, we further develop a successive interference cancellation-based semi-blind detection scheme, where a bilinear generalized approximate message passing algorithm is utilized for joint channel and signal estimation (JCSE), while the embedded RI is exploited for ambiguity elimination. Particularly, a rank selection approach and a RI-aided initialization strategy are incorporated to reduce the algorithmic computational complexity and to enhance the JCSE reliability, respectively. Besides, four enabling techniques are integrated to satisfy the stringent latency and reliability requirements of massive URLLC. Numerical results demonstrate that the proposed semi-blind detection framework offers a better scalability-latency-reliability tradeoff than the state-of-the-art detection schemes dedicated to sourced or unsourced RA.

Uncertainty of Low‐Degree Space Gravimetry Observations: Surface Processes Versus Earth's Core Signal

AbstractSpace gravity measurements have been mainly used to study the temporal mass variations at the Earth's surface and within the mantle. Nevertheless, mass variations due to the Earth's core might be observable in the gravity field variations as measured by Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On satellites. Earth's core dynamical processes inferred from geomagnetic field measurements are characterized by large‐scale patterns associated with low spherical harmonic degrees of the potential fields. To study these processes, the use of large spatial and inter‐annual temporal filters is needed. To access gravity variations related to the Earth's core, surface effects must be corrected, including hydrological, oceanic or atmospheric loading (Newtonian attraction and mass redistribution). However, these corrections for surface processes add errors to the estimates of the residual gravity field variations enclosing deep Earth's signals. As our goal is to evaluate the possibility to detect signals of core origin embedded in the residual gravity field variations, a quantification of the uncertainty associated with gravity field products and geophysical models used to minimize the surface process signatures is necessary. Here, we estimate the dispersion for GRACE solutions as about 0.34 cm of equivalent water height (EWH) or 20% of the total signal. Uncertainty for hydrological models is as large as 0.89–2.10 cm of EWH. We provide estimates of Earth's core signals whose amplitudes are compared with GRACE gravity field residuals and uncertainties. The results presented here underline how challenging is to get new information about the dynamics of the Earth's core via high‐resolution, high‐accuracy gravity data.

New Array Designs for DoA Estimation of Non-Circular Signals With Reduced Mutual Coupling

Recently, many sparse arrays are proposed to achieve increased uniform degrees of freedom (uDoFs) for the direction of arrival (DoA) estimation of non-circular sources. In light of this recent attention, the high number of uDoFs is the primary motivation for designing those sparse array structures. Although both the number of uDoF and mutual coupling can influence the DoA estimation performance. However, mutual coupling is not considered in many of those sparse array configurations. In this paper, two optimized shifted coprime arrays with sum-difference coarrays, called OSCAwSDCa-I and OSCAwSDCa-II, are proposed to reduce mutual coupling. The OSCAwSDCa-I and OSCAwSDCa-II are obtained by rearranging actual sensors of the augmented coprime array. The proposed array designs have closed-form expressions for their sensor locations and achievable uDoFs. Additionally, it is proven that the first three weights of OSCAwSDCa-II are independent of the array size. Moreover, the number of virtual sensors co-locating at the same lag is reduced. Consequently, smaller weight values are obtained, resulting in more robustness against mutual coupling. Finally, extensive simulations show that OSCAwSDCa-I and OSCAwSDCa-II can achieve comparable uDoFs concerning the existing array structures. In the presence of strong mutual coupling, simulation results demonstrate the robustness of OSCAwSDCa-II more than other compared configurations.

Time of arrival estimation for underwater acoustic signal using multi-feature fusion

Underwater target detection (UTD) is widely applied in ocean exploration. A key issue for the time-of-arrival (ToA)-based UTD algorithm is to localize the first direct path in an underwater multipath channel. However, the conventional Matched Filter (MF) suffers from a deterioration of the estimation accuracy when the direct arrival of the received signal is non-strongest. Therefore, this paper proposes a multi-feature fusion ToA estimation algorithm. First, a multi-decision strategy including the coarse ToA range estimation and the fine ToA estimation is proposed to realize a balance between computational complexity and estimation accuracy. Second, the coarse ToA range estimation is realized by multi-level spectrum analysis. Then, the coarse ToA range is used to localize the fine ToA utilizing the multi-feature fusion ToA estimation. Finally, the simulation and experimental results are provided to validate the proposed ToA estimation algorithm. It clearly shows that without training data, the proposed algorithm can not only deal with the multipath acoustic channel problem but also improve the accuracy of the ToA estimation compared to other algorithms.

Hierarchical nuclear norm penalization for multi-view data integration.

The prevalence of data collected on the same set of samples from multiple sources (i.e., multi-view data) has prompted significant development of data integration methods based on low-rank matrix factorizations. These methods decompose signal matrices from each view into the sum of shared and individual structures, which are further used for dimension reduction, exploratory analyses, and quantifying associations across views. However, existing methods have limitations in modeling partially-shared structures due to either too restrictive models, or restrictive identifiability conditions. To address these challenges, we propose a new formulation for signal structures that include partially-shared signals based on grouping the views into so-called hierarchical levels with identifiable guarantees under suitable conditions. The proposed hierarchy leads us to introduce a new penalty, hierarchical nuclear norm (HNN), for signal estimation. In contrast to existing methods, HNN penalization avoids scores and loadings factorization of the signals and leads to a convex optimization problem, which we solve using a dual forward-backward algorithm. We propose a simple refitting procedure to adjust the penalization bias and develop an adapted version of bi-cross-validation for selecting tuning parameters. Extensive simulation studies and analysis of the genotype-tissue expression data demonstrate the advantages of our method over existingalternatives.

Multi-sensor random sample consensus for instantaneous frequency estimation of multi-component signals

An instantaneous frequency estimation algorithm is developed for multi-component signals acquired through multiple sensors. The proposed method estimates the mixing matrix for the strongest source in the given multi-sensor recording. The information of the mixing matrix is used to extract the corresponding source through spatial filtering. The spatially filtered signal is then analyzed using adaptive directional time-frequency distribution. The instantaneous frequencies of the extracted sources are then estimated through the Random sample consensus (RANSAC) algorithm that involves the detection of peaks, fitting of the instantaneous frequency curve using Fourier series approximation, and evaluating the instantaneous frequency estimate using an objective function. Both the mixing matrix and instantaneous frequency information is used to remove the corresponding source from the mixture signal by employing time-frequency and spatial filtering. The process mentioned above is then repeated till the instantaneous frequencies of all the sources have been estimated. Once the instantaneous frequencies of all the sources have been estimated, each source's instantaneous frequency is re-estimated by removing all the other components using time-frequency and spatial filtering. Till convergence, the process is iterated. Through numerical results, it is shown that the proposed method performs better than the relevant methods in the state of the art.

A Novel Optimization Strategy of Sidelobe Suppression for Pulse Compression Weather Radar

The solid-state transmitters are widely adopted for weather radars, where pulse compression is operated to provide the required sensitivity and range resolution. Therefore, effective sidelobe suppression strategies must be employed, especially for weather observation. Currently, many methods can suppress the sidelobe to a very low level in the case of point targets or uniformly distributed targets. However, in strong convection weather process, the weather echo amplitude lies in a wide dynamic range and the main lobe of weak target is prone to being contaminated by the sidelobe of strong target, causing the degradation of weather fundamental data estimation, even generating artifacts and affecting the quantitative precipitation evaluation. In this paper, we propose a novel strategy which is the further processing of a general pulse compression radar to mitigate the effects of sidelobes. The proposed method is called the predominant component extraction (PCE), in which the re-weighting processing is operated after pulse compression, and then the echo of each bin is optimized and its energy will approach the real targets in each bin. It can improve the estimation of weak signals or even eliminate the artifact at the edge of the scene. Numerical simulation experiments and real-data verifications are implemented to validate the feasibility and superiority. It is noted that the proposed method has no requirement on the transmitted waveform and can be realized only by adding a step after pulse compression in the actual system.

Evaluation of L-band GPS signal attenuation to multiple vegetations using ground-based measurements

ABSTRACT Global Navigation Satellite System Reflectometry (GNSS-R) is a remote sensing technique that can be regarded as a bistatic radar system. GNSS-R uses GNSS signals as signal sources and obtains the Earth’s surface environmental parameters, such as soil moisture (SM), by receiving the L-band microwave signal reflected from the Earth’s surface. However, surface vegetation could be one of the main factors influencing the accuracy of GNSS-R land applications since plants, including branches and leaves, attenuate GNSS signals. Additionally, the evaluation of signal attenuation caused by the plant canopy is quite difficult. In this paper, we study the attenuations of received L1- and L2-band GPS signals to the vegetation leaf area index (LAI) for different types of plants. The relationship between the attenuation of the GPS signal-to-noise ratio (SNR) (both above and below the canopy) and the LAI is established through field experiments. The results show that the mean SNR received in the L2 band is lower than that in the L1 band for each satellite but with a larger standard deviation (SD). The sensitivities of L1- and L2-band signals to the LAI are revealed, revealing greater sensitivity and a relatively good Pearson correlation coefficient (R) for lower elevation angles and vegetation biomass. In addition, the sensitivity and R of L2-band signals to the LAI are lower than those of L1-band signals. This study is significantly valuable for improving the quantitative representation of error estimates for GNSS-R SM retrieval. The established model can be employed in GNSS land applications and aid in solving signal surface-scattering problems in which accurate signal estimates are important.

Direction of Arrival Estimation for Single Microelectromechanical Systems Vector Hydrophone Using Modified Wavelet Packet De-Noising

The complex and variable ocean environment introduces complex noise to the signal received by microelectromechanical systems (MEMS) vector hydrophones, which seriously affects target location. To improve the detection efficiency and broaden the application range of vector hydrophone, this paper proposed a novel modified Wavelet packet de-noising method, known as the Wavelet packet energy (WPE) de-noising method. This method utilizes Shannon entropy to determine the optimal decomposition levels of Wavelet packets, and calculates the energy of reconstructed signals in each frequency band to construct feature vectors. The Wavelet packet with the larger energy is selected to reconstruct the signal until the root mean square error of the original signal and the reconstructed signal is minimized. thereby obtaining an optimal solution. The performance of this proposed method is evaluated through both indoor and outdoor experiments. The experimental results demonstrate that the developed method is effective in Direction of Arrival (DOA) estimation of single frequency signals and is feasible in low signal-to-noise ratio situations. The method improves the azimuth discrimination ability of the Minimum Variance Distortionless Response algorithm, and DOA estimation errors are all within 1°. Furthermore, the indoor and outdoor experiments also demonstrate that the WPE de-noising method solves the group delay and distortion of signals caused by conventional filtering.

Measuring direction of arrival in cognitive radio systems using variable step adaptive learning for spectrum sensing

In wireless communications cognitive radios are rapidly growing technology and it is used in medical telemetry applications to provide treatment to remote located patients on time. Generally, interferences are occurred due to a shortage of frequency range and spectrums. To avoid these interferences new technologies are developed with cognitive radios. In the received signal there are some noisy signals are occurred due to signal estimation is not done properly. For estimating input signal primary user and secondary user signals direction of arrival is taken into consideration. To improve further output and to remove noise signals of received signal proposed an adaptive learning methodology. In this paper a regularization based variable step adaptive learning algorithm (VSALA) is developed for avoiding minimum disturbance constraint functions in cost function. Further regularization factor and step size combination results a faster convergence when compared to conventional normalized adaptive algorithm (NALA). The proposed learning methodology is analysed for different threshold values like 0.1, 0.5 and 1.0 for estimating the direction of arrival (DOA) and their corresponding beam forming patterns are shown.

Frequency estimation for underwater CW pulse by interpolation on fractional Fourier coefficients based on amplitude ratio method

Fast and accurate estimation of sinusoidal signals plays an important role in many fields like communications, radar, sonar, etc. In underwater signal processing applications, sinusoidal signals usually take the form of CW pulses in most practical applications, therefore, zero-padding and duty-cycle show their great importance to the estimation of sinusoidal signals. In this paper, a high-precision estimation algorithm of sinusoidal signal is proposed, which combines amplitude ratio algorithm and fractional Fourier coefficient interpolation algorithm. The proposed algorithm uses the adjacent spectral line ratio algorithm instead of the Fourier coefficient maximum amplitude discrete spectral line search algorithm for coarse estimation, and modifies the traditional interpolation method. The proposed algorithm improves the Fourier coefficient interpolation algorithm by combining zero-padded signals to achieve the accurate frequency estimation for zero-padded sinusoidal signal. The performance of the algorithm is also in accordance with theoretical level for zero-padded signals, which is a great improvement over the frequency estimation algorithm for non-padded signals as well as the algorithm for zero-padding signals. The theoretical results are verified by extensive computer simulations which show that the proposed algorithm can both achieve better results for zero-padding cases and maintain comparable performance with competing algorithms for the non-padded signal. Therefore, the algorithm can be better applied to practical underwater detection or communication signals.

Whole genome sequencing for more accurate TMB and mutational signatures in common cancers.

e15173 Background: Due to the dramatic decrease in cost of genome sequencing, we are entering the era of whole genome sequencing (WGS) at $100. Tumor mutational burden (TMB) and mutational signatures have been introduced as potential prognostic/predictive cancer biomarkers and mostly assessed by targeted cancer gene sequencing panels. However, they are truly whole genome-wide events and thus more accurately estimated by whole genome sequencing data. Methods: We analyzed WGS data produced by Genome Insight Inc. that encompass > 1,300 breast, lung, and hepatocellular carcinoma samples, collectively. Our genome-wide TMBs (wgsTMB) were systematically compared with the TMB estimates by targeted sequencing methods, such as conventional whole-exome sequencing (WES) and targeted panel sequencing from FoundationOne CDx (F1CDx). In parallel, we leveraged genome-wide somatic mutations for validating mutational signatures projected from the targeted sequencing methods. Results: Pembrolizumab is FDA approved for solid tumors with high TMB (≥ 10 mutations/Mb) assessed by F1CDx. This criterion classified that 546 of the tumors (41%) in our cohort as high F1CDx TMB (f1TMB), who may benefit from immunotherapy. However, the high f1TMB classification was inconsistent to the genome-wide TMB classification ( p < 2.2e-16). For example, 132 high f1TMB tumors (24.1%; 132/546) showed lower than the median wgsTMB estimate. On the flip side, 32% of the low f1TMB tumors (251/784) demonstrate higher than median wgsTMB estimate (see table). Although there is no approved criterion in WGS that defines hypermutators, our data illustrates that WGS will be helpful for redefining the criteria for selecting patients for immune checkpoint inhibitors. The wgsTMB was also an essential factor for accurate estimation of mutational signatures from targeted sequencing. For example, in high TMB tumors, mutational signature estimated from WES showed on average 0.87 cosine similarity with signatures from WGS. However, the correlation was substantially reduced in low TMB tumors, showing only ~0.64 on average. Particularly, ~12% of breast tumors with considerable levels of homologous recombination deficiency (HRD) signatures by WGS were not captured by whole-exome sequencing based approaches. Further exploration should be performed with WGS to select proper patients for targeted therapies, including PARP inhibitors. Conclusions: Despite the current guidelines depending on targeted sequencing in precision oncology, we showed that whole-genome sequencing can deliver more accurate cancer genome profiles, such as, but not limited to, TMB and mutational signatures. Given its more comprehensive capabilities and affordable cost, we believe that WGS-based precision oncology is medically necessary. [Table: see text]

Parameter estimation of LFM signals based on time reversal

In this paper, parameter estimation of linear frequency modulation (LFM) signals containing additive white Gaussian noise is studied. Because the center frequency estimation of an LFM signal is affected by the error propagation effect, resulting in a higher signal to noise ratio (SNR) threshold, a parameter estimation method for LFM signals based on time reversal is proposed. The proposed method avoids SNR loss in the process of estimating the frequency, thus reducing the SNR threshold. The simulation results show that the threshold is reduced by 5 dB compared with the discrete polynomial transform (DPT) method, and the root-mean-square error (RMSE) of the proposed estimator is close to the Cramer-Rao lower bound (CRLB).