Coherent Algorithm Research Articles

Model and long-term coherent accumulation basic algorithm for the reflected signal with non-zero higher derivative range to radar target

The purpose of the work was to substantiate a mathematical model of the signal reflected from moving radar target at the time of its observation, which determines the need to take into account the range migration and its derivatives up to the third order, inclusive, and the Doppler frequency migration, and the basic algorithm of long-term coherent accumulation for the reflected signal. The algorithm provides for the calculation of the “fast” spectra samples, within each repetition period, time, the phase alignment of the spectra samples by multiplying by the correcting phase factors determined by the expected parameters of the target movement and the number of the repetition period, the summation of the spectral samples in the “slow” time, the multiplication of the result by complex frequency response of a matched filter of a single signal and performing an inverse Fourier transform. The performance of the algorithm is illustrated by the results of computer simulation.

Ultra-High Resolution Imaging Method for Distributed Small Satellite Spotlight MIMO-SAR Based on Sub-Aperture Image Fusion.

Small satellite synthetic aperture radar (SAR) has become a new development direction of spaceborne SAR due to its advantages of flexible launch, short development cycle, and low cost. However, there are fewer researches on distributed small satellite multiple input multiple output (MIMO) SAR. This paper proposes an ultra-high resolution imaging method for the distributed small satellite spotlight MIMO-SAR, which applies the sub-aperture division technique and the sub-aperture image coherent fusion algorithm to MIMO-SAR. After deblurring the sub-aperture signal, the large bandwidth signal is obtained by using an improved time domain bandwidth synthesis (TBS) method, and then the ultra-high resolution image is obtained by using a sub-aperture image coherent fusion algorithm. Simulation results validate the feasibility and effectiveness of the proposed approach.

Deep neural network-aided coherent integration method for maneuvering target detection

Generalized Radon-Fourier transform (GRFT) is a classical long-time coherent integration method for radar maneuvering target detection. GRFT, whose core is to achieve motion parameter estimation via searching, can almost reach the optimal detection performance but heavily suffers from the high computational cost. Motivated by the fact that motion parameter estimation is essentially a non-linear mapping from the radar echo to the target’s motion parameters, one can use a deep neural network (DNN), a kind of modeling tool that can learn complex mappings from training data, to directly realize this mapping, thus alleviating the heavy computational burden brought by the searching efforts. Based on this idea, a DNN-aided long-time coherent integration algorithm, which can be viewed as a fast implementation of GRFT, is proposed in this paper. More specifically, we first use a pre-trained DNN to roughly estimate the motion parameters of the target to be detected from the radar echo, and then accomplish the coherent integration of the target for detection via a fine grid search in the neighborhood of the obtained rough estimation results. Finally, simulation results demonstrate that the proposed algorithm can achieve the detection performance close to that of GRFT but with a much lower computational cost.

Forest Canopy Height Estimation Using Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) Technology Based on Full-Polarized ALOS/PALSAR Data

Forest canopy height is a basic metric characterizing forest growth and carbon sink capacity. Based on full-polarized Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) data, this study used Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technology to estimate forest canopy height. In total the four methods of differential DEM (digital elevation model) algorithm, coherent amplitude algorithm, coherent phase-amplitude algorithm and three-stage random volume over ground algorithm (RVoG_3) were proposed to obtain canopy height and their accuracy was compared in consideration of the impacts of coherence coefficient and range slope levels. The influence of the statistical window size on the coherence coefficient was analyzed to improve the estimation accuracy. On the basis of traditional algorithms, time decoherence was performed on ALOS/PALSAR data by introducing the change rate of Landsat NDVI (Normalized Difference Vegetation Index). The slope in range direction was calculated based on SRTM (Shuttle Radar Topography Mission) DEM data and then introduced into the s-RVoG (sloped-Random Volume over Ground) model to optimize the canopy height estimation model and improve the accuracy. The results indicated that the differential DEM algorithm underestimated the canopy height significantly, while the coherent amplitude algorithm overestimated the canopy height. After removing the systematic coherence, the overestimation of the RVoG_3 model was restrained, and the absolute error decreased from 23.68 m to 4.86 m. With further time decoherence, the determination coefficient increased to 0.2439. With the introduction of range slope, the s-RVoG model shows improvement compared to the RVoG model. Our results will provide a reference for the appropriate algorithm selection and optimization for forest canopy height estimation using full-polarized L-band synthetic aperture radar (SAR) data for forest ecosystem monitoring and management.

Quantifying Information via Shannon Entropy in Spatially Structured Optical Beams.

While information is ubiquitously generated, shared, and analyzed in a modern-day life, there is still some controversy around the ways to assess the amount and quality of information inside a noisy optical channel. A number of theoretical approaches based on, e.g., conditional Shannon entropy and Fisher information have been developed, along with some experimental validations. Some of these approaches are limited to a certain alphabet, while others tend to fall short when considering optical beams with a nontrivial structure, such as Hermite-Gauss, Laguerre-Gauss, and other modes with a nontrivial structure. Here, we propose a new definition of the classical Shannon information via the Wigner distribution function, while respecting the Heisenberg inequality. Following this definition, we calculate the amount of information in Gaussian, Hermite-Gaussian, and Laguerre-Gaussian laser modes in juxtaposition and experimentally validate it by reconstruction of the Wigner distribution function from the intensity distribution of structured laser beams. We experimentally demonstrate the technique that allows to infer field structure of the laser beams in singular optics to assess the amount of contained information. Given the generality, this approach of defining information via analyzing the beam complexity is applicable to laser modes of any topology that can be described by well-behaved functions. Classical Shannon information, defined in this way, is detached from a particular alphabet, i.e., communication scheme, and scales with the structural complexity of the system. Such a synergy between the Wigner distribution function encompassing the information in both real and reciprocal space and information being a measure of disorder can contribute into future coherent detection algorithms and remote sensing.

Effective Connectivity for Default Mode Network Analysis of Alcoholism.

Introduction: With the recent technical advances in brain imaging modalities such as magnetic resonance imaging, positron emission tomography, and functional magnetic resonance imaging (fMRI), researchers' interests have inclined over the years to study brain functions through the analysis of the variations in the statistical dependence among various brain regions. Through its wide use in studying brain connectivity, the low temporal resolution of the fMRI represented by the limited number of samples per second, in addition to its dependence on brain slow hemodynamic changes, makes it of limited capability in studying the fast underlying neural processes during information exchange between brain regions. Materials and Methods: In this article, the high temporal resolution of the electroencephalography (EEG) is utilized to estimate the effective connectivity within the default mode network (DMN). The EEG data are collected from 20 subjects with alcoholism and 25 healthy subjects (controls), and used to obtain the effective connectivity diagram of the DMN using the Partial Directed Coherence algorithm. Results: The resulting effective connectivity diagram within the DMN shows the unidirectional causal effect of each region on the other. The variations in the causal effects within the DMN between controls and alcoholics show clear correlation with the symptoms that are usually associated with alcoholism, such as cognitive and memory impairments, executive control, and attention deficiency. The correlation between the exchanged causal effects within the DMN and symptoms related to alcoholism is discussed and properly analyzed. Conclusion: The establishment of the causal differences between control and alcoholic subjects within the DMN regions provides valuable insight into the mechanism by which alcohol modulates our cognitive and executive functions and creates better possibility for effective treatment of alcohol use disorder.

Longtime Coherent Integration Algorithm for High-Speed Maneuvering Target Detection Using Space-Based Bistatic Radar

A space-based bistatic radar system, typically composed of a satellite as transmitter and an aircraft as receiver, has a larger surveillance area, stronger resistance to damage, and more superior early warning capability for high-speed targets when compared with monostatic radar system. However, the echo signal is more challenging to process because of the high speed of both receiver and transmitter. There are two main problems to be solved in space-based bistatic radar signal processing: constructing geometric model in line with the actual situation to derive the echo signal model and improving detection ability on the basis of the longtime coherent integration algorithm. This article addresses the problem of space-based bistatic radar echo signal modeling and coherent integration for detecting high-speed maneuvering targets. A novel algorithm based on generalized second-order keystone transform and modified product cubic phase function (GSKT-MPCPF) is proposed. First, the bistatic radar system model is constructed and the signal scattered from the moving target is derived. Aiming at seeking a general algorithm which is suitable for space-based bistatic radar system, the GSKT-MPCPF is presented to correct the linear range migration (LRM), the quadratic range migration (QRM), and the Doppler frequency migration (DFM), and estimate the motion parameters of targets simultaneously. The simulations of mono-target and multitargets validate the proposed method. Furthermore, we perform comparisons with several relative algorithms and results show that the proposed algorithm has superior performance.

Внедрение инновационного программно-аппаратного комплекса пассивной акустики для диагностики технического состояния скважин

To ensure operational stability of the unified gas supply system depending on the seasonal gas demand fluctuations, the underground gas storage facilities are effectively used. Well integrity diagnostics using the advanced logging techniques facilitate timely identification of potential problems (pinpointing cross-flows behind casing, detecting leaks in production casing and other well construction components, locating sustained annulus pressure sources etc.), and, thereby extension of the safe operation life of functioning of the underground gas storage facilities. The results of the complex of field and geophysical studies in several underground gas storage wells are given in this article. Different types of the underground gas storages are reviewed: the ones located in an aquifer, salt dome, and mature field. The diagnostics was carried out based on the complex of downhole logging including a passive acoustic tool. The passive acoustics hardware and software technology can capture and interpret acoustic signals generated by the wellbore or reservoir flows. To recognise and interpret the captured signals, the temporal coherence algorithms, data wavelet filtration, and neural networks are used. The use of this technology allows to significantly improve the safety of well operation. The information obtained is used both in the preparation of industrial safety expert reports and for the development of effective programs for major repairs of the underground gas storage wells located in the depleted gas and water fields, as well as in the salt caverns. Hardware and software technology of passive acoustics is being used in almost all the major oil and gas provinces in the world, receiving recognition from the largest domestic and foreign companies, however, this is the first time that underground gas storage facilities are used to check the technical condition of the wells.

Floor of log: a novel intelligent algorithm for 3D lung segmentation in computer tomography images

This work presents a high-performance approach for 3D lung segmentation tasks in computer tomography images using a new intelligent machine learning algorithm called Floor of Log(FoL). The Support Vector Machine was used to learn the better parameter of the FoL algorithm using the parenchyma and its border as labels. Sensitivity, Matthews Correlation Coefficient (MCC), Hausdorff Distance (HD), Dice, Accuracy (ACC), and Jaccard were used to evaluate the proposed algorithm. The FoL was compared with recent 3D region growing, 3D Adaptive Crisp Active Contour, 3D OsiriX toolbox, and Level-set algorithm based on the coherent propagation method algorithms. The FoL algorithm achieves good results with approximately 19 s in the most significant result in an exam with 430 slices and presents similarity indexes achieving HD 3.5, DICE 83.63, and Jaccard 99.73 and qualitative indexes achieving Sensitivity 83.87, MCC 83.08, and ACC 99.62. The proposed approach of this work showed a simple and powerful algorithm to segment lung in computer tomography images of the chest region by combining similar textures, highlighting the lung structure. The FoL was presented as a new supervised clustering algorithm which can be trained to achieve better results and attached to other approaches as Convolutional Deep Neural Networks applications.

Improving fault delineation using maximum entropy multispectral coherence

Knowledge of fault geometry plays an important role in reservoir modeling and characterization. Seismic attributes, such as volumetric dip, coherence, and curvature, provide an efficient and objective tool to extract fault geometry attributes. Traditionally, we use noise-attenuated full-bandwidth seismic data to compute coherence followed by smoothing, sharpening, and skeletonization. However, different stratigraphic reflectors with relatively similar waveforms and amplitudes juxtaposing across a fault will algorithmically appear to be continuous, with the resulting fault image being broken. This leads to pseudo fault breakpoints and challenges the accurate extraction of other fault geometric attributes. Because the phase of the similar reflections across the faults varies with different spectral components, such nonstratigraphic alignments occur for only a few spectral components such that a multispectral coherence algorithm produces more continuous fault images. We have evaluated the influence of spectral voice selection and spectral decomposition algorithm on the quality of fault imaging in multispectral coherence images using a 3D seismic survey acquired in the Taranaki Basin, New Zealand. Of the algorithms evaluated, we find that the high-resolution maximum-entropy-based multispectral coherence method provides better results than those based on other spectral decomposition algorithms, which especially improves the fault continuity. However, the lateral resolution of fault imaging in multispectral coherence is decreased compared to the full-bandwidth coherence, because the fault image is smeared when we combine the coherence volumes computed using different spectral voices. We perform a fault enhancement workflow on the maximum-entropy-based multispectral coherence volume to improve the lateral resolution of fault imaging, which helps delineate the minor faults.

A Novel Radar Detection Method for Sensing Tiny and Maneuvering Insect Migrants

The use of radar to monitor insect migration is of great significance for pest control and biological migration mechanism research. However, migrating insects usually have small radar-cross-section (RCS) and are accompanied by maneuvering. The current radar detection algorithms mainly have contradictions in detection performance and computational complexity. So it is difficult for traditional radar detection algorithms to detect them effectively. Hence, a novel coherent integration detection algorithm based on dynamic programming (DP) and fractional Fourier transforming (FrFT) is proposed. By combining the advantages of DP and FrFT, the proposed DP-FrFT method can quickly search the target track, and simultaneously perform parameters estimation and motion compensation, achieving high integration gain with relatively low time consumption. The high efficiency of the method is verified with a large number of simulations and sufficient field experiments.

Coherent integration algorithm for random pulse repetition interval radar based on iterative adaptive approach

An iterative adaptive approach (IAA) based algorithm is presented to realise coherent integration for the random pulse repetition interval radar. The proposed method firstly removes the effect of Doppler ambiguity via phase compensation, and then accomplishes target focusing via conducting the azimuth IAA and range inverse fast Fourier transform. Theoretical analysis and numerical experiments have verified the effectiveness of the proposed method.

A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes

BackgroundStatistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes. This study proposes a family of statistical models under a unified partial-linear single-index (PLSI) modeling framework, to assess the joint effects of environmental factors for continuous, categorical, time-to-event, and longitudinal outcomes. All PLSI models consist of a linear combination of exposures into a single index for practical interpretability of relative direction and importance, and a nonparametric link function for modeling flexibility.MethodsWe presented PLSI linear regression and PLSI quantile regression for continuous outcome, PLSI generalized linear regression for categorical outcome, PLSI proportional hazards model for time-to-event outcome, and PLSI mixed-effects model for longitudinal outcome. These models were demonstrated using a dataset of 800 subjects from NHANES 2003–2004 survey including 8 environmental factors. Serum triglyceride concentration was analyzed as a continuous outcome and then dichotomized as a binary outcome. Simulations were conducted to demonstrate the PLSI proportional hazards model and PLSI mixed-effects model. The performance of PLSI models was compared with their counterpart parametric models.ResultsPLSI linear, quantile, and logistic regressions showed similar results that the 8 environmental factors had both positive and negative associations with triglycerides, with a-Tocopherol having the most positive and trans-b-carotene having the most negative association. For the time-to-event and longitudinal settings, simulations showed that PLSI models could correctly identify directions and relative importance for the 8 environmental factors. Compared with parametric models, PLSI models got similar results when the link function was close to linear, but clearly outperformed in simulations with nonlinear effects.ConclusionsWe presented a unified family of PLSI models to assess the joint effects of exposures on four commonly-used types of outcomes in environmental research, and demonstrated their modeling flexibility and effectiveness, especially for studying environmental factors with mixed directional effects and/or nonlinear effects. Our study has expanded the analytical toolbox for investigating the complex effects of environmental factors. A practical contribution also included a coherent algorithm for all proposed PLSI models with R codes available.

An efficient and robust scheme for the implementation of eigenstructure-based coherency algorithms

The coherence attribute has been widely used to detect faults and channels as well as other structural and stratigraphic discontinuities since its introduction. Currently, the most commonly used algorithm for coherence computation is the eigenstructure-based coherence method (C3). When seismic data are of a low signal-to-noise ratio, more traces should be involved in coherence computation to improve the robustness to noise; thus, a larger analysis window shall be used. However, this will lead to increased time costs and blurred coherence images. To address these issues, we have adopted a modified eigenstructure-based coherency algorithm (C3m) based on an inverse distance weighting method and dimensionality reduction of the covariance matrix for eigendecomposition in the case of a large lateral analysis window whose size is larger than [Formula: see text] (nine traces). To further improve the computational efficiency, the add-drop algorithm is also used for covariance matrix construction. In the case of large analysis windows, the time cost by C3m is less than the time cost by C3 as demonstrated in synthetic and field data studies; also, the coherence results computed via C3m are superior to those computed via the traditional C3 method, and the new method is more favorable to seismic interpretation. Thus, our method shall be a potentially efficient and robust alternative to C3 for coherence attribute computation.

A Novel Long-Time Coherent Integration Algorithm for Doppler-Ambiguous Radar Maneuvering Target Detection

A long-time coherent integration could effectively improve the detection ability of radar for maneuvering targets. Nevertheless, the Doppler ambiguity and frequency migration caused by the high speed and acceleration severely degrade the detection performance. In this regard, a novel coherent integration algorithm is proposed, particularly for maneuvering targets with Doppler ambiguity. Specifically, the acceleration is firstly estimated by scaled non-uniform fast Fourier transform (SNuFFT). Then, the scaled periodic discrete Fourier transform (SPDFT), which periodically extends the observable Doppler scope of discrete Fourier transform (DFT), is proposed to estimate the unambiguous Doppler frequency. Finally, the grating lobes are significantly suppressed via product operation, and coherent integration is achieved after phase compensation. To alleviate the computational burden and eliminate the brute force searching procedure, an efficient implementation based on chirp-z transform (CZT) is also derived. Analysis shows that the proposed algorithm achieves a good balance between computational complexity and anti-noise performance. Extensive simulations and real measured radar data are conducted to verify the proposed algorithm.

The CARTOPRIME module with the Coherent Mapping algorithm for ablation of complex (scar-related) atrial tachycardia.

The CARTOPRIME module with the Coherent Mapping algorithm for ablation of complex (scar-related) atrial tachycardia.

Quantitative error analysis for non-mechanical phase-controlled beam steering based on symmetrical radial sub-aperture coherence algorithm

ABSTRACT Based on the fundamental of traditional Sub-aperture coherence (SAC) algorithm, Radial Sub-aperture coherence (RSAC) and Symmetrical Radial Sub-aperture coherence (SRSAC) have been designed to achieve non-mechanical beam steering with ultra-high precision and stability by utilising two-dimensional Liquid Crystal Optical Phased Array (LCOPA). Both of them inherit the original advantages, that is, to eliminate the local pointing accuracy defects and greatly improve the angular resolution of the beam steering system. Meanwhile, the steering angle error caused by the aperture variation and the alignment error can be also effectively restricted by SRSAC. In this paper, the essential mathematical framework of SRSAC and its affiliated local error elimination method was established to supplement the theoretical support of these phase generation algorithms. By dissecting the generation mechanism of each pointing error branch, the theoretical formulae with great universality are finally proposed, which provide visualised and necessary references for the specific application in other beam control situations.

3-D Short-Range Imaging With Irregular MIMO Arrays Using NUFFT-Based Range Migration Algorithm

3-D imaging with irregular planar multiple-input-multiple-output (MIMO) arrays is discussed. Due to signal acquisition on irregular spatial sampling grids by using these antenna arrays, the fast Fourier transform (FFT)-based imaging algorithms cannot readily be used for image formation. To avoid the application of computationally intensive coherent summation algorithms such as filtered backprojection or Kirchhoff migration, we propose a nonuniform FFT (NUFFT)-based MIMO Range Migration Algorithm (i.e., NUFFT-based MIMO-RMA) for efficient microwave imaging. The algorithm exploits NUFFT to reconstruct the wavenumber–domain spectra related to each Fourier frequency. It is generic and applicable to 3-D imaging with irregular planar MIMO arrays. The effects of irregular spatial sampling and signal bandwidth on the imaging performance and computational efficiency of the proposed algorithm are analyzed. Finally, some numerical simulations and experimental results are presented to demonstrate the performance of the proposed imaging algorithm.

Approximating the maximum tibial coverage in total knee arthroplasty does not necessarily result in implant malrotation

Traditionally, the practice of the tibial component placement in total knee arthroplasty has focused on achieving maximum coverage without malrotation. However, the concept of maximizing coverage has not been well defined or researched and yet biased results are often produced. This study aimed to evaluate the effect of a prioritizing maximum coverage positioning strategy on the rotational alignment by using a strict computer algorithm. Computed tomographic scans of 103 tibial specimens were used to reconstruct three-dimensional tibia models. A virtual surgery was performed to generate the resection plane with a posterior slope of 7° on the proximal tibia. Symmetrical and anatomical tibial components were placed and analyzed with an automated program designed for approximating the maximum coverage based on the coherent point drift algorithm. We found that the average tibial coverage achieved across all specimens and implants was 85.62 ± 3.65%, ranging from 83.64 ± 4.10% to 86.69 ± 3.07%. When placed for maximal tibial coverage, the mean degree of rotation related to the Insall line was − 0.73° ± 4.53° for all subjects, 23% of the tibial components were malrotated. The average percentage position of the baseplate anteroposterior axis over the patellar tendon was 26.95 ± 14.71% from the medial edge. These results suggest that with specific design and proper placement of the component, approximating the maximum tibial coverage in total knee arthroplasty does not necessarily result in implant malrotation. The current tibial baseplates have shown good performance on the coverage when aligned parallel to the Insall line with the anteroposterior axis positioned between the medial 1/3 and medial 1/6 of the patella tendon.

Slope-Shift Keying LoRa-Based Modulation

LoRa, a low-power long-range physical-layer communication method, is one of the emerging technologies in low-power wide-area networks (LPWANs). In LoRa, a linearly varying-frequency up chirp and its cyclic shifts act as an orthonormal basis set for message representation. The number of orthonormal basis functions dictates the achievable bit rate of a LoRa system. This article aims to increase the achievable data rate of a conventional LoRa system by adding a set of new orthonormal basis functions to the existing set. Specifically, we use a linearly varying-frequency down chirp and its cyclic shifts to generate the second set of orthonormal basis functions in the proposed scheme, named slope-shift-keying LoRa (SSK-LoRa) modulation. We also develop both low-complexity optimum coherent and noncoherent detection algorithms for the proposed SSK-LoRa modulation. We then obtain closed-form analytical and tight approximations for the bit and symbol error probabilities of noncoherent detection in a Rayleigh fading environment. Our proposed SSK-LoRa scheme is shown to outperform the state-of-the-art LoRa-based modulation schemes.