Coherent Algorithm Research Articles

Three-dimensional geosteering coherence attributes for deep-formation discontinuity detection

Deep-formation oil/gas exploration is a key objective in the geophysical field, and structural and stratigraphic discontinuities, such as faults and channels, usually contribute to the construction of traps and reservoirs. Coherence has been used successfully to identify these abnormal features in seismic amplitude volumes. However, the current coherence algorithms seldom involve the geologic concept. We propose geosteering coherence attributes by implementing the coherence calculation perpendicular to the direction of the structural trend in a 3D curved plane. We estimate a group of time lags between the original analysis trace and each original neighboring trace along a certain spatial direction by using dip scanning. For each spatial direction, we subsequently construct two new model traces by weighting phase traces derived from the complex seismic traces, in which time lags are eliminated. We then use the new model traces to compute the crosscorrelation coefficients for each spatial direction. We finally obtain the 3D geosteering coherence attributes by taking the minimum values among the modulus of the crosscorrelation coefficients along different spatial directions to approximately characterize the coherence perpendicular to the structural trend in a 3D curved plane. An example of the 3D physical modeling involving fracture groups and faults embedded in the deep formation is used to demonstrate the effectiveness of the 3D geosteering coherence attributes. The applications on two real 3D seismic data sets of sand reservoirs from western deep formation illustrate that our method can alleviate the influence of dipping strata and can highlight subtle structures. Compared to the conventional coherence method, our method can highlight subtle geologic structures more and better, suggesting that it may be serve as a future tool for detecting the distribution of geologic abnormalities in deep exploration.

Evaluation on Directed Functional Brain Connectivity during the Expert Rifle Pre-shot Period

Experts require long-term and intense practice to acquire extraordinary motor skills that be known as the brain function regulator. However, the brain function network mechanism of rifle shooters is still unclear. The generalized orthogonalized partial directed coherence (gOPDC) algorithm and local efficiency significance analysis method (LESA) was applied to quantify the difference in directed functional networks between expert and novice rifle shooters during the pre-shot period. The more directed functional connections were observed in alpha and low-beta frequency bands as compared to other bands. Moreover, comparing with the novice’s fluctuant connection, the values of connection (P3→C3) strength were increasing steadily in the experts during the pre-shot period. Fewer connections in left hemisphere networks were obtained in the experts than in the novices. The results validated the “neural efficiency” hypothesis in experts. Moreover, the strength of the functional connection (P3→C3) in the alpha and beta bands serves as a distinguishing feature between experts and novices.

Manoeuvring target detection based on keystone transforms and conjugates time reversing transform

This letter investigates the coherent integration problem for the manoeuvring target with a constant acceleration. A fast coherent integration algorithm based on the keystone transform (KT) and the conjugate time reversing transform (CTRT) is proposed to mitigate the range migration (RM) and the Doppler frequency migration (DFM). Firstly, the proposed algorithm employs the KT to remove the RM efficiently. Subsequently, the CTRT is adopted to remove the DFM. Finally, the Fourier transform is conducted to achieve the coherent integration. The computation of the proposed algorithm is in the same order with the KT. Real data processing results demonstrate the effectiveness of the proposed algorithm.

Computationally efficient coherent detection and parameter estimation algorithm for maneuvering target

In this paper, a computationally efficient coherent detection and parameter estimation algorithm via symmetric autocorrelation function (SAF) and scaled Fourier transform (i.e., SAF-SFT) is proposed, involving range cell migration (RCM) and Doppler spread (DS) within the coherent integration (CI) time. In particular, the first SAF and SFT operations are applied to achieve the range and velocity estimations after the generalized keystone transform. With the estimations, the remaining RCM induced by target’s velocity could be removed and the target signal could be extracted along the range cell. Then the second SAF and SFT operations are performed on the extracted signal, where the target energy could be coherent integrated and the acceleration estimation can be obtained. Cross term of SAF-SFT is also analyzed and its characteristic indicates the applicability in the scenario of multi-targets. Detailed comparisons of SAF-SFT with several typical algorithms with respect to computational cost, detection probability and parameter estimation ability show that the SAF-SFT could strike a balance between computational cost and detection probability as well as the estimation performance. Simulation results and real test experiment are given to verify the SAF-SFT based approach.

Balanced Error Distribution for Internet Video Retargeting

With the progress of network technology, online video is widely used by users and popular. Image and video retargeting techniques have been proposed in the recent past by researchers around the world, low-complexity algorithms allow video to be transmitted more quickly. Our Balanced Error Distribution (BED) retargeting method follows the same concept like uniform scaling, but only removes unimportant regions, balancing main objects’ error distribution. Priority map is proposed in this paper to maintain the structure of straight lines and irregular shape of objects without deforming complex image contents, which may be altered in traditional seam carving for complex image or video. In addition, the proposed mechanism adopts the general temporal coherence algorithm with strict condition (two continuous seams must be close as possible) to maintain visual continuity, such that the resulting video will not look shaky due to sudden changes in the background. The proposed method not only resizes the video with the retention of important information but also maintains the structural properties of objects in different kinds of videos.

An Efficient ISAR Imaging of Targets with Complex Motions Based on a Quasi-Time-Frequency Analysis Bilinear Coherent Algorithm.

The inverse synthetic aperture radar (ISAR) imaging for targets with complex motions has always been a challenging task due to the time-varying Doppler parameter, especially at the low signal-to-noise ratio (SNR) condition. In this paper, an efficient ISAR imaging algorithm for maneuvering targets based on a noise-resistance bilinear coherent integration is developed without the parameter estimation. First, the received signals of the ISAR in a range bin are modelled as a multicomponent quadratic frequency-modulated (QFM) signal after the translational motion compensation. Second, a novel quasi-time-frequency representation noise-resistance bilinear Radon-cubic phase function (CPF)-Fourier transform (RCFT) is proposed, which is based on the coherent integration of the energy of auto-terms along the slope line trajectory. In doing so, the RCFT also effectively suppresses the cross-terms and spurious peaks interference at no expense of the time-frequency resolution loss. Third, the cross-range positions of target’s scatters in ISAR image are obtained via a simple maximization projection from the RCFT result to the Doppler centroid axis, and the final high-resolution ISAR image is thus produced by regrouping all the range-Doppler frequency centroids. Compared with the existing time-frequency analysis-based and parameter estimation-based ISAR imaging algorithms, the proposed method presents the following features: (1) Better cross-term interference suppression at no time-frequency resolution loss; (2) computationally efficient without estimating the parameters of each scatters; (3) higher signal processing gain because of 2-D coherent integration realization and its bilinear function feature. The simulation results are provided to demonstrate the performance of the proposed method.

Efficient connectivity analysis of electroencephalogram in the pre-shot phase of rifle shooting based on causality method

The directed functional connectivity in cerebral cortical is the key to understanding the pattern of the behavioral tissue. This process was studied to explore the directed functional network of rifle shooters at cerebral cortical rhythms from electroencephalogram (EEG) data, aiming to provide neurosciences basis for the future development of accelerating rifle skill learning method. The generalized orthogonalized partial directed coherence (gOPDC) algorithm was used to calculate the effective directed functional connectivity of the experts and novices in the pre-shot period. The results showed that the frontal, frontal-central, central, parietal and occipital regions were activated. Moreover, the more directed functional connections numbers in right hemispheres were observed compared to the left hemispheres. Furthermore, as compared to experts, novices had more activated regions, the stronger strength of connections and the lower value of the global efficiency during the pre-shot period. Those indirectly supported the conclusion that the novices needed to recruit more brain resources to accomplish tasks, which was consistent with "neural efficiency" hypothesis of the functional cerebral cortical in experts.

Fast Coherent Differential Imaging on Ground-based Telescopes Using the Self-coherent Camera

Direct imaging and spectral characterization of exoplanets using extreme adaptive optics (ExAO) is a key science goal of future extremely large telescopes and space observatories. However, quasi-static wavefront errors will limit the sensitivity of this endeavor. Additional limitations for ground-based telescopes arise from residual AO-corrected atmospheric wavefront errors, generating millisecond-lifetime speckles that average into a halo over a long exposure. A solution to both of these problems is to use the science camera of an ExAO system as a wavefront sensor to perform a fast measurement and correction method to minimize these aberrations as soon as they are detected. We develop the framework for one such method based on the self-coherent camera (SCC) to be applied to ground-based telescopes, called Fast Atmospheric SCC Technique (FAST). We show that with the use of a specially designed coronagraph and coherent differential imaging algorithm, recording images every few milliseconds allows for a subtraction of atmospheric and static speckles while maintaining a close to unity algorithmic exoplanet throughput. Detailed simulations reach a contrast close to the photon noise limit after 30 seconds for a 1 % bandpass in H band on both 0$^\text{th}$ and 5$^\text{th}$ magnitude stars. For the 5th magnitude case, this is about 110 times better in raw contrast than what is currently achieved from ExAO instruments if we extrapolate for an hour of observing time, illustrating that sensitivity improvement from this method could play an essential role in the future detection and characterization of lower mass exoplanets.

A new approach to selecting coherent pixels for ground-based SAR deformation monitoring

Ground-Based Synthetic Aperture Radar (GBSAR) is a flexible field-based remote sensing technology that, together with interferometric SAR (InSAR), has proven to be a powerful and effective tool for deformation monitoring. The Small Baseline Subset (SBAS) algorithm represents a typical advanced InSAR technique that extracts distributed scatterers from a network of interferograms for the measurement of time series displacement. However, it is well known that coherent points are variable from one interferogram to another, which renders time series analysis complicated. This study therefore proposes an effective approach to selecting coherent pixels from a network of interferograms, aiming to maximize the density of selected pixels and optimize the reliability of GBSAR time series analysis. A pixel is selected for the entire analysis if its coherent phase is capable of forming a full-rank coefficient matrix in the network inversion. A full-rank matrix means the pixel-dependent subset network is connected. Combining with the accurate estimation of coherence and interferometric phase based on sibling pixels identified from non-local windows, the proposed approach enables the selection of not only qualified partially coherent pixels but also persistent scatterers. The proposed approach has been incorporated into a bespoke GBSAR time series analysis chain for deformation monitoring, from which a mean velocity map, displacement time series and atmospheric phase delays can be determined. To validate the approach, experiments on two GBSAR datasets were performed. In both studies, sufficient coherent pixels were selected, suggesting the feasibility of the proposed coherent pixel selection algorithm. Displacement time series at the level of a few sub-millimeters were observed for both datasets, indicating the feasibility of the newly-developed GBSAR time series analysis chain for deformation monitoring, which is believed to lead to a wide range of scientific and practical applications.

Radar High-Speed Maneuvering Target Detection Based on Three-Dimensional Scaled Transform

This paper presents a novel coherent high-speed maneuvering target detection algorithm, which is based on the three-dimensional (3-D) scaled transform. This algorithm coherently integrates the echo energy into a peak in a 3-D parameter space and estimates the target's radial velocity and acceleration simultaneously by the peak detection technique. Thereafter, compensating off the across range unit and Doppler frequency migration via estimations, this algorithm coherently integrates the echo energy in the range-Doppler space and uses the constant false alarm rate technique to complete the target detection. The cross term of the proposed algorithm is also analyzed and its characteristic indicates the applicability in the scenario of multiple targets. The computational complexity, resolution, peak-to-sidelobe level (PSL), and detection performance are analyzed and compared with several typical algorithms, which leads us to conclude that the proposed algorithm can strike a balance between the computational complexity and detection performance with high resolution and PSL. Finally, experiments with the real measured radar data are conducted to verify the proposed algorithm.

2-D Coherent Integration Processing and Detecting of Aircrafts Using GNSS-Based Passive Radar

Long time coherent integration is a vital method for improving the detection ability of global navigation satellite system (GNSS)-based passive radar, because the GNSS signal is not radar-designed and its power level is very low. For aircraft detection, the large range cell migration (RCM) and Doppler frequency migration (DFM) will seriously affect the coherent processing of azimuth signals, and the traditional range match filter will also be mismatched due to the Doppler-intolerant characteristic of GNSS signals. Accordingly, the energy loss of 2-dimensional (2-D) coherent processing is inevitable in traditional methods. In this paper, a novel 2-D coherent integration processing and algorithm for aircraft target detection is proposed. For azimuth processing, a modified Radon Fourier Transform (RFT) with range-walk removal and Doppler rate estimation is performed. In respect to range compression, a modified matched filter with a shifting Doppler is applied. As a result, the signal will be accurately focused in the range-Doppler domain, and a sufficiently high SNR can be obtained for aircraft detection with a moving target detector. Numerical simulations demonstrate that the range-Doppler parameters of an aircraft target can be obtained, and the position and velocity of the aircraft can be estimated accurately by multiple observation geometries due to abundant GNSS resources. The experimental results also illustrate that the blind Doppler sidelobe is suppressed effectively and the proposed algorithm has a good performance even in the presence of Doppler ambiguity.

Design and Implementation of a Transmit/Receive Ultrasound Phased Array for Brain Applications

Focused ultrasound phased array systems have attracted increased attention for brain therapy applications. However, such systems currently lack a direct and real-time method to intraoperatively monitor ultrasound pressure distribution for securing treatment. This study proposes a dual-mode ultrasound phased array system design to support transmit/receive operations for concurrent ultrasound exposure and backscattered focal beam reconstruction through a spherically focused ultrasound array. A 256-channel ultrasound transmission system was used to transmit focused ultrasonic energy (full 256 channels), with an extended implementation of multiple-channel receiving function (up to 64 channels) using the same 256-channel ultrasound array. A coherent backscatter-received beam formation algorithm was implemented to map the point spread function (PSF) and focal beam distribution under a free-field/transcranial environment setup, with the backscattering generated from a strong scatterer (a point reflector or a microbubble-perfused tube) or a weakly scattered tissue-mimicking graphite phantom. Our results showed that PSF and focal beam can be successfully reconstructed and visualized in free-field conditions and can also be transcranially reconstructed following skull-induced aberration correction. In vivo experiments were conducted to demonstrate its capability to preoperatively and semiquantitatively map a focal beam to guide blood-brain barrier opening. The proposed system may have potential for real-time guidance of ultrasound brain intervention, and may facilitate the design of a dual-mode ultrasound phased array for brain therapeutic applications.

A Coherent Detection and Velocity Estimation Algorithm for the High-Speed Target Based on the Modified Location Rotation Transform

High-speed target often suffers from the range walk (RW) problem within the process of long-time coherent integration (CI). The aim of this paper is to present a new coherent detection algorithm, i.e., modified location rotation transform (MLRT), which could effectively correct the RW and estimate the velocity of the high-speed target. In particular, the MLRT algorithm could rotate the coordinate locations of echo data to remove the RW and estimate the velocity via searching the rotation angles. Then, the CI of target's energy is obtained via the Fourier transform along the slow-time direction. The advantage of the MLRT is that it could achieve superior integration and detection performance and avoid the blind speed sidelobe effect without increasing the computational complexity, in comparison with other representative algorithms. Simulations and real data processing are provided to evaluate the efficacy and feasibility of the proposed algorithm.

Coherence based on spectral variance analysis

The eigenstructure-based coherence attribute is a type of efficient and mature tool for mapping geologic edges such as faults and/or channels in the 3D seismic interpretation. However, the eigenstructure-based coherence algorithm is sensitive to low signal-to-noise ratio seismic data, and the coherence results are affected by the dipping structures. Due to the large energy gap between the low- and high-frequency components, the low-frequency components play the principal role in coherence estimation. In contrast, the spectral variance balances the difference between the low- and high-frequency components at a fixed depth. The coherence estimation based on amplitude spectra avoids the effect of the time delays resulting from the dipping structures. Combining the spectral variance with the amplitude spectra avoids the effect of dipping structures and enhances the antinoise performance of the high-frequency components. First, we apply the short-time Fourier transform to obtain the time-frequency spectra of seismic data. Next, we compute the variance values of amplitude spectra. Then, we apply the fast Fourier transform to obtain the amplitude spectra of spectral variance. Finally, we calculate the eigenstructure coherence by using the amplitude spectra of spectral variance as the input. We apply the method to the theoretical models and practical seismic data. In the Marmousi velocity model, the coherence estimation using the amplitude spectra of the spectral variance as input shows more subtle discontinuities, especially in deeper layers. The results from field-data examples demonstrate that the proposed method is helpful for mapping faults and for improving the narrow channel edges’ resolution of interest. Therefore, the coherence algorithm based on the spectral variance analysis may be conducive to the seismic interpretation.

Multispectral coherence

Seismic coherence is a routine measure of seismic reflection similarity for interpreters seeking structural boundary and discontinuity features that may be not properly highlighted on original amplitude volumes. One mostly wishes to use the broadest band seismic data for interpretation. However, because of thickness tuning effects, spectral components of specific frequencies can highlight features of certain thicknesses with higher signal-to-noise ratio than others. Seismic stratigraphic features (e.g., channels) may be buried in the full-bandwidth data, but can be “lit up” at certain spectral components. For the same reason, coherence attributes computed from spectral voice components (equivalent to a filter bank) also often provide sharper images, with the “best” component being a function of the tuning thickness and the reflector alignment across faults. Although one can corender three coherence images using red-green-blue (RGB) blending, a display of the information contained in more than three volumes in a single image is difficult. We address this problem by combining covariance matrices for each spectral component, adding them together, resulting in a “multispectral” coherence algorithm. The multispectral coherence images provide better images of channel incisement, and they are less noisy than those computed from the full bandwidth data. In addition, multispectral coherence also provides a significant advantage over RGB blended volumes. The information content from unlimited spectral voices can be combined into one volume, which is useful for a posteriori/further processing, such as color corendering display with other related attributes, such as petrophysics parameters plotted against a polychromatic color bar. We develop the value of multispectral coherence by comparing it with the RGB blended volumes and coherence computed from spectrally balanced, full-bandwidth seismic amplitude volume from a megamerge survey acquired over the Red Fork Formation of the Anadarko Basin, Oklahoma.

An Automated Calibration Method of Ultrasonic Probe Based on Coherent Point Drift Algorithm

Ultrasound-based navigation, as a non-invasive and non-radiation image guiding system, is becoming a research focus in minimally invasive navigation surgery, and calibration between an ultrasonic probe and a 3D vision device is one of the key technologies for ultrasonic registration-based navigation. In this paper, a phantom model was designed as a benchmark for calibration. Both iterative closet point (ICP) and coherent point drift (CPD) algorithms are chosen as point cloud registration methods to implement calibration between ultrasonic scanned points and original phantom points to set up the relationship between the ultrasonic probe and the 3D vision device. Because of large topological difference between the ultrasound scanned points and the points from model, ICP algorithm cannot complete the registration, but the CPD algorithm could implement the registration automatically. The average errors of the center point position for each cavity were 1.50, 1.31, and 1.19 mm, respectively, and the average errors of the axis for each cavity were 0.85°, 0.61°, and 0.99°, respectively. Experiment results showed that the average error of calibration by this method satisfies acquirements of most orthopedic surgeries, and the fully automatic implementation of ultrasonic image processing and subsequent calculation is suitable for on-line calibration and verification in surgery.

Quadrotor Formation Control Method Based on Graph and Consistency Theory

Abstract This paper introduces graph and group system consistency theory and puts forward a quadrotor formation control method. The quadrotor is described as a second-order integrator dynamic system, and the relative position deviation of different quadrotors is used to describe formation. According to the communication topology relationship between quadrotors, the formation is modeled by graph theory. The fusion of the pilot-follow and graph theory method is analyzed, and a second-order coherence algorithm with a pilot is presented. With this algorithm, the quadrotors can complete behaviors just as formation rally and formation mobility etc. Finally the paper verifies the availability of the proposed method through simulation tests.

Multiscale Fractures Characterization Based on Ant Colony Optimization and Two-Dimensional Variational Mode Decomposition

Hydrocarbon exploration in fractured reservoirs requires accurate estimates of fracture characteristics such as fracture orientations and fracture development degrees. The coherence method, which measures the degree of similarity between the multichannel seismic data, can image fractures by detecting variation anomalies in the seismic signals. However, it is weak in precisely positioning fractures and is sensitive to noise. We use an improved ant colony optimization (ACO) algorithm that adopts both coherence data and edge detection data as heuristic factor to refine the coherence data. As a distinctive time–frequency analysis method, variational mode decomposition (VMD) is a flexible method with excellent time–frequency resolution. We propose a measure factor combining mutual information and energy entropy to judge how many intrinsic mode functions to be decomposed are most suitable. Since fracture characterization is similar to image analysis, we propose a novel method combining ACO-coherence with a two-dimensional VMD (2D-VMD) to meticulously depict fractures at different scales and different directions. First, the seismic coherence data are obtained via a dip scanning coherence algorithm. Then, the coherence data are refined by the improved ACO algorithm. We extract the slice along the target stratum from the ACO-coherence data, and the slice is decomposed into different frequency band images using the 2D-VMD method. After applying the method to real seismic data from a typical fractured reservoir, it is clear that the method we propose is capable of revealing fractures at different scales and different directions with high precision.

Effective multi-stage error control algorithms for robust 3D video transmission over wireless networks

The Three-Dimensional Video (3DV) contains diverse video streams taken by different cameras around an object. Thence, it is an imperative assignment to fulfill efficient compression to match the future resource limitations, whilst preserving a decisive reception 3DV quality. The efficient 3DV communication over wireless networks has become a recent considerable hot issue due to the restricted resources and the presence of severe channel errors. The high-rate 3DV encoding and transmission over mobile or Internet are vulnerable to packet losses due to the existence of heavy channel losses and limited bandwidth. Therefore, this paper presents efficient multi-stage error control algorithms for reliable 3DV transmission over error-prone wireless channels. At the encoder, the error resilience schemes of context adaptive variable length coding, slice structured coding, and explicit flexible macro-block ordering are utilized. At the decoder, a joint approach of a directional interpolation error concealment algorithm and a directional textural motion coherence algorithm is proposed to conceal the corrupted color frames. For the concealment of the lost depth frames, an encoder independent decoder dependent depth-assisted error concealment algorithm is suggested. Moreover, the weighted overlapping block motion and disparity compensation algorithm is exploited to choose the candidate concealment Motion Vectors (MVs) and Disparity Vectors (DVs). Furthermore, an improved recursive Bayesian filtering algorithm is utilized as a refinement stage to smooth the remaining errors in the previously selected candidate MVs and DVs for achieving better 3DV quality. Simulation results on several 3DV sequences show that the proposed algorithms achieve adequate objective and subjective 3DV quality performance at severe packet loss rates compared to the state-of-the-art algorithms.

Direct positioning maximum likelihood estimator using TDOA and FDOA for coherent short‐pulse radar

The intra-pulse phase change caused by frequency difference of arrival (FDOA) is very little and even omitted due to extremely short duration of emitter pulses that the joint location using time difference of arrival (TDOA) and FDOA cannot be realised. To solve this problem, a multi-pulse coherent accumulation algorithm of direct position determination (DPD) using the TDOA and FDOA is proposed in this study. Through establishing coherent signal model of short pulses which is deterministic but unknown, the maximum likelihood estimator for localisation of a stationary emitter using multiple moving receivers is derived by taking the DPD approach under additive Gaussian white noise environment. This study also derives a signal-specific Cramer-Rao lower bound (CRLB) of DPD, modelling the signal as a deterministic unknown. The CRLB is more applicable to some specific (non-stationary, non-Gaussian) signals, especially electromagnetic signals. Simulation results show that the proposed method achieves a significant performance improvement in locating accuracy over the existing methods and its accuracy is close to the CRLB in normal signal-to-noise ratio.