Signal Decorrelation Research Articles

Multi-Source DoA Estimation of EM Waves Impinging Spherical Antenna Array With Unknown Mutual Coupling Using Relative Signal Pressure Based Multiple Signal Classification Approach

Spherical antenna array (SAA) is a configuration that scans almost all the radiation sphere with constant directivity. It finds applications in spacecraft and satellite communication. Multiple signal classification (MUSIC) is a widely used multiple source direction-of-arrival (DoA) estimation method because of its low complexity implementation in practical applications. Conversely, it is susceptible to noise, which consequently affects its accuracy of localization. In this paper, MUSIC-based methods that operate at low signal-to-noise ratio (SNR) are developed via relative electromagnetic (EM) wave pressure measurements of a SAA. The proposed methods are the relative pressure MUSIC (RP-MUSIC), and in spherical domain (SH-RP-MUSIC). The developed SH-RP-MUSIC algorithm is in spherical domain thereby allows frequency-smoothing approach for the de-correlation of the coherent source signals towards an enhanced accuracy of localization. Both RP-MUSIC and SH-RP-MUSIC algorithms developed have the ability to estimate the number of active sources that is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of the conventional MUSIC algorithm. Numerical experiments were used to demonstrate the adequacy of the developed algorithms. In addition, measured data from experiment, which is the practically acceptable way to examine any procedure is employed to demonstrate the merits of the developed algorithms against the conventional MUSIC algorithm and other recent multiple source localization method in literature. Finally, in order to achieve DoA estimations with adequate localization accuracy at low SNR using SAA, SH-RP-MUSIC algorithm is a better choice.

A Data-Driven Approach to Audio Decorrelation

The degree of correlation between two audio signals entering the ears is known to have a significant impact on the spatial perception of a sound image. Audio signal decorrelation is therefore a widely used tool in various applications within the field of spatial audio processing. This paper explores for the first time the use of a data-driven approach for audio decorrelation. We propose a convolutional neural network architecture that is trained with the help of a state-of-the-art reference decorrelator. The proposed approach is evaluated using music and applause signals by means of objective evaluations as well as through a listening test. The proposed approach can serve as a proof of concept to address common limitations of existing decorrelation techniques in future work, which include introduction of temporal smearing and coloration artifacts and the production of a limited number of mutually uncorrelated output signals.

A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau

Active rock glaciers (ARGs) are important permafrost landforms in alpine regions. Identifying ARGs has mainly relied on visual interpretation of their geomorphic characteristics with optical remote sensing images, while mapping ARGs from their kinematic features has also become popular in recent years. However, a thorough comparison of geomorphic- and kinematic-based inventories of ARGs has not been carried out. In this study, we employed a multi-temporal interferometric synthetic aperture radar (InSAR) technique to derive the mean annual surface displacement velocity over the Daxue Shan, Southeast Tibet Plateau. We then compiled a rock glacier inventory by synergistically interpreting the InSAR-derived surface displacements and geomorphic features based on Google Earth images. Our InSAR-assist kinematic-based inventory (KBI) was further compared with a pre-existing geomorphic-based inventory (GBI) of rock glaciers in Daxue Shan. The results show that our InSAR-assist inventory consists of 344 ARGs, 36% (i.e., 125) more than that derived from the geomorphic-based method (i.e., 251). Only 32 ARGs in the GBI are not included in the KBI. Among the 219 ARGs detected by both approaches, the ones with area differences of more than 20% account for about 32% (i.e., 70 ARGs). The mean downslope velocities of ARGs calculated from InSAR are between 2.8 and 107.4 mm∙a−1. Our comparative analyses show that ARGs mapping from the InSAR-based kinematic approach is more efficient and accurate than the geomorphic-based approach. Nonetheless, the completeness of the InSAR-assist KBI is affected by the SAR data acquisition time, signal decorrelation, geometric distortion of SAR images, and the sensitivity of the InSAR measurement to ground deformation. We suggest that the kinematic-based approach should be utilized in future ARGs-based studies such as regional permafrost distribution assessment and water storage estimates.

Novel DOA Estimation of Coherent Signal based on Virtual Array

Conventional direction-of-arrival (DOA) estimations perform poorly in coherent signals due to the rank loss of spatial covariance matrix, and most of the existing methods solve the problem at the expense of Degree of Freedom (DOF) or accuracy. In this paper, a novel DOA estimation technique for coherent signal is presented based on virtual array. The method comprises three steps: Firstly, the covariance matrix is generated according to the output signals of the array. Then, the first row and first column are selected from the matrix according to the data model of each element of the covariance matrix, which can be regarded as a uniform and continuous single snapshot signal of virtual array. Finally, the forward and backward spatial smoothing is used to achieve decorrelation of the single snapshot signal of the virtual array element. The proposed method has no loss of DOF and maintains high accuracy for low signal-to-noise ratio (SNR) conditions. The feasibility of the proposed method is proved by theoretical derivation, and its effectiveness is also verified by numerical results.

Analysis of tissue displacement induced by high-intensity focused ultrasound exposure for coagulation monitoring

Decorrelation between two ultrasound images has been investigated for real-time monitoring of lesion formation caused by high-intensity focused ultrasound (HIFU) exposure. However, the origin of the decorrelation remains unclear. In this study, the distributions of tissue displacement were obtained from numerical simulation and experimental data, and the latter was compared to the decorrelation map. The results show the possibility that decorrelation of ultrasonic signals can be explained by thermal expansion and apparent displacement caused by the temperature dependence of the speed of sound.

GNSS Ocean Bistatic Statistical Scattering in the Time-Varying Regime: Modeling and Correlation Properties

The main contributions to signal decorrelation in Global Navigation Satellite Systems Reflectometry (GNSS-R) over the ocean are known to be the platforms motion and the sea surface roughness. These two actions play at different timescales: of the order of few milliseconds, the former and hundreds of milliseconds, the latter. Currently, bistatic scattering models as well as simulators do not provide a rationale for sea surface correlation, whose nature is essentially random. In this work, the scattered signal is modeled as the sum of incoherent contributions arising from single spatial regions that correspond to delay-Doppler cells in the glistening zone. The statistical model of the signal scattered by a moving sea surface is derived by introducing a process for appearing and disappearing of local scattering cores on the sea surface. The scatterers lifetime is governed by a birth–death–immigration process; in the limit of a large number of scatterers, a compound Gaussian process arises, whose temporal statistics are investigated. The analysis of the signal correlation and of the correlation time is considered for the case of an airborne receiver.

Optimal radar cross section estimation in synthetic aperture radar with planar antenna array

The optimization problem of statistical synthesis of the method for radar cross section estimation in synthetic aperture radar with planar antenna array is solved. The desired radar cross section is given as a statistical characteristic of a spatially inhomogeneous complex scattering coefficient of the studying media. In fact it is developed new methods of inverse problems solution not with respect to the restoration of coherent images in the form of spatial distribution of complex scattering coefficient but with respect to the statistical characteristics of inhomogeneous (spatially nonstationary) random processes. The electrophysical parameters of surfaces and their statistical characteristics are considered as functions of spatial coordinates. The maximum likelihood method was chosen as the optimization method. The obtained results make it possible to determine the multichannel structure, the optimal method of surface observation and the potential spatial resolution in aerospace scatterometric radars with antenna array. Optimal operations for processing space-time signals are determined and a modified method for synthesizing antenna aperture is proposed, which in contrast to the classical algorithm for synthesizing antenna aperture that integrates the product of the received signal and the reference signal equal to a single signal additionally implements the decorrelation of signals reflected from the earth's surface, The new operation of the scattered signals decorrelation consists in their integration with the space-time inverse correlation function. To confirm the reliability of the results obtained, simulation modeling of the classical method for the synthesis of coherent images and the proposed optimal one was carried out. From the analysis of the results it flows that propose method has higher quality and smaller size of spackle noise. The results obtained in the article can be used to develop and substantiate the requirements for the tactical and technical characteristics of promising aerospace-based scatterometric radars with planar phased antenna arrays.

Distributed chromatic processing at the interface between retina and brain in the larval zebrafish

Larval zebrafish (Danio rerio) are an ideal organism for studying color vision, as their retina possesses four types of cone photoreceptors, covering most of the visible range and into the UV.1,2 Additionally, their eye and nervous systems are accessible to imaging, given that they are naturally transparent.3-5 Recent studies have found that, through a set of wavelength-range-specific horizontal, bipolar, and retinal ganglion cells (RGCs),6-9 the eye relays tetrachromatic information to several retinorecipient areas (RAs).10-13 The main RA is the optic tectum, receiving 97% of the RGC axons via the neuropil mass termed arborization field 10 (AF10).14,15 Here, we aim to understand the processing of chromatic signals at the interface between RGCs and their major brain targets. We used 2-photon calcium imaging to separately measure the responses of RGCs and neurons in the brain to four different chromatic stimuli in awake animals. We find that chromatic information is widespread throughout the brain, with a large variety of responses among RGCs, and an even greater diversity in their targets. Specific combinations of response types are enriched in specific nuclei, but there is no single color processing structure. In the main interface in this pathway, the connection between AF10 and tectum, we observe key elements of neural processing, such as enhanced signal decorrelation and improved chromatic decoding.16,17 A richer stimulus set revealed that these enhancements occur in the context of a more distributed code in tectum, facilitating chromatic signal association in this small vertebrate brain.

Two approaches to multiuser detection over fading channels

In this paper, two different receiver structures to multiuser detection that are appropriate for the code-division multiple-access systems with antenna arrays in fading channels are investigated and compared. We analyze and compare the performance of the two different multiuser detection structures for uplink or downlink channels. The number of elements of receiving antenna array may be limited in the downlink channel due to the small size of receivers. We assume a synchronous system, but it can be easily extended to an asynchronous system. The first approach is based on the distributed decorrelator where the signal decorrelation is performed by each receiving antenna element independently and decorrelated outputs are combined according to the maximum ratio. The second approach is the central decorrelator where the signal decorrelation is performed once collectively on the outputs from all elements of receiving antenna array. Both decorrelators provide the same performance in the additive white Gaussian noise channels. The distributed decorrelator provides the better performance in flat fading channels. We employ the decorrelator to demonstrate our results. The results discussed in the present paper can be extended to other configurations such as the blind adaptive space-time multiuser detection.

Adaptive optical focusing through perturbed scattering media with a dynamic mutation algorithm

Optical imaging through or inside scattering media, such as multimode fiber and biological tissues, has a significant impact in biomedicine yet is considered challenging due to the strong scattering nature of light. In the past decade, promising progress has been made in the field, largely benefiting from the invention of iterative optical wavefront shaping, with which deep-tissue high-resolution optical focusing and hence imaging becomes possible. Most of the reported iterative algorithms can overcome small perturbations on the noise level but fail to effectively adapt beyond the noise level, e.g., sudden strong perturbations. Reoptimizations are usually needed for significant decorrelation to the medium since these algorithms heavily rely on the optimization performance in the previous iterations. Such ineffectiveness is probably due to the absence of a metric that can gauge the deviation of the instant wavefront from the optimum compensation based on the concurrently measured optical focusing. In this study, a square rule of binary-amplitude modulation, directly relating the measured focusing performance with the error in the optimized wavefront, is theoretically proved and experimentally validated. With this simple rule, it is feasible to quantify how many pixels on the spatial light modulator incorrectly modulate the wavefront for the instant status of the medium or the whole system. As an example of application, we propose a novel algorithm, the dynamic mutation algorithm, which has high adaptability against perturbations by probing how far the optimization has gone toward the theoretically optimal performance. The diminished focus of scattered light can be effectively recovered when perturbations to the medium cause a significant drop in the focusing performance, which no existing algorithms can achieve due to their inherent strong dependence on previous optimizations. With further improvement, the square rule and the new algorithm may boost or inspire many applications, such as high-resolution optical imaging and stimulation, in instable or dynamic scattering environments.

Multi-Temporal Speckle Filtering of Polarimetric P-Band SAR Data over Dense Tropical Forests: Study Case in French Guiana for the BIOMASS Mission

The purpose of this paper is twofold, considering first the generalization of a multichannel speckle filter in order to handle temporal stacks of polarimetric SLC SAR data, and secondly the development of an ad hoc performance indicator based on the Polarimetric Orientation Angle (POA) in order to better estimate the resulting speckle reduction than the standard Equivalent Number of Looks (ENL) over densely vegetated regions, like tropical forests. Being based on the ability of PolSAR measurements to retrieve ground slopes through dense vegetation, this performance indicator requires the use of low frequencies such as P-band, as well as fully polarimetric data. This study has thereby a particular interest in the context of the upcoming BIOMASS spaceborne mission whose launch is scheduled in 2023, and makes use of data from the TropiSAR airborne campaign initiated in the early stage of the mission developments. Conducted over several test sites of tropical dense forests in French Guiana, this campaign gives us the opportunity herein to exploit P-band temporal stacks with repeated time intervals transposable to BIOMASS in terms of signal decorrelation. The application of the generalized multichannel speckle filter to the Paracou test site dataset reveals the limitations of the standard ENL analytical formula to assess speckle reduction in the case of spatially correlated media like dense forests, and for this purpose the interest of the correlation between POA and azimuthal slopes computed from an independent Digital Surface Model.

A Robotically Steerable Guidewire With Forward-Viewing Ultrasound: Development of Technology for Minimally-Invasive Imaging.

The current standard of care for peripheral chronic total occlusions involves the manual routing of a guidewire under fluoroscopy. Despite significant improvements in recent decades, navigation remains clinically challenging with high rates of procedural failure and iatrogenic injury. To address this challenge, we present a proof-of-concept robotic guidewire system with forward-viewing ultrasound imaging to allow visualization and maneuverability through complex vasculature. A 0.035" guidewire-specific ultrasound transducer with matching layer and acoustic backing was designed, fabricated, and characterized. The effect of guidewire motion on signal decorrelation was assessed with simulations and experimentally, driving the development of a synthetic aperture beamforming approach to form images as the transducer is steered on the robotic guidewire. System performance was evaluated by imaging wire targets in water. Finally, proof-of-concept was demonstrated by imaging an ex vivo artery. The designed custom transducer was fabricated with a center frequency of 15.7 MHz, 45.4% fractional bandwidth, and 31dB SNR. In imaging 20 μm wire targets at a depth of 6mm, the lateral -6dB target width was 0.25 ± 0.03mm. The 3D artery reconstruction allowed visualization of vessel wall structure and lumen. Initial proof-of-concept for an ultrasound transducer-tipped steerable guidewire including 3D image formation without an additional sensor to determine guidewire position was demonstrated for a sub-mm system with an integrated ultrasound transducer and a robotically-steered guidewire. The developed forward-viewing, robotically-steered guidewire may enable navigation through occluded vascular regions that cannot be crossed with current methods.

Injection-locked 256 QAM WDM coherent transmissions in the C- and L-bands.

We demonstrate WDM 256 QAM coherent transmissions with injection locking in the C- and L-bands and compare the transmission performance in the two bands. Although four-wave mixing (FWM) is more significant in an L-band EDFA than in a C-band EDFA, the FWM did not accumulate through the transmission and the FWM components were hidden by the ASE noise level. Since the FWM was weakened by the decorrelation of the WDM signals during the transmission, the transmission performance in the L-band was the same as that in the C-band. The injection locking circuit enabled precise carrier-phase synchronization between a data signal and a local oscillator regardless of the transmission band. By using this circuit, we successfully transmitted 58.2 and 57.6 Tbit/s 256 QAM WDM signals over 160 km with a spectral efficiency of 12 bit/s/Hz in the C- and L-bands, respectively.

Approaches to quantify optical coherence tomography angiography metrics.

Optical coherence tomography (OCT) has revolutionized the field of ophthalmology in the last three decades. As an OCT extension, OCT angiography (OCTA) utilizes a fast OCT system to detect motion contrast in ocular tissue and provides a three-dimensional representation of the ocular vasculature in a non-invasive, dye-free manner. The first OCT machine equipped with OCTA function was approved by U.S. Food and Drug Administration in 2016 and now it is widely applied in clinics. To date, numerous methods have been developed to aid OCTA interpretation and quantification. In this review, we focused on the workflow of OCTA-based interpretation, beginning from the generation of the OCTA images using signal decorrelation, which we divided into intensity-based, phase-based and phasor-based methods. We further discussed methods used to address image artifacts that are commonly observed in clinical settings, to the algorithms for image enhancement, binarization, and OCTA metrics extraction. We believe a better grasp of these technical aspects of OCTA will enhance the understanding of the technology and its potential application in disease diagnosis and management. Moreover, future studies will also explore the use of ocular OCTA as a window to link ocular vasculature to the function of other organs such as the kidney and brain.

An Adaptive ICA-Based Cross-Correlation Techniques for Water Pipeline Leakage Localization Utilizing Acousto-Optic Sensors

This paper describes the implementation of an adaptive ICA (Independent Component Analysis) based cross-correlation technique to process the noisy output from Acoustic optic sensor to locate the leaks in water pipes. In Digital Signal Processing methods, cross-correlation technique plays a prominent role in detecting the leak signals. In a real-time scenario, the leak signals get convolved with the impulse response of the pipe. In such cases, the conventional cross-correlation technique fails to detect the leak. This paper implements the maximization of non-gaussianity of ICA techniques along with wavelet decomposition as a preprocessing stage of frequency domain cross-correlation. The decomposition of the sensor data using wavelet reduces the computational complexity of the ICA. The adaptiveness of ICA in the proposed technique is more suitable for real-time water leak detection system. The probable benefit of using adaptive ICA is to enforce strong decorrelation and separation of the real-time leak signal from the convoluted impulse response of the pipe with noise. The above algorithm is validated using different experiments. The experimental result shows that the proposed method offers high accuracy of detecting the leak location compared to the conventional methods.

Dopaminergic Modulation of Signal Processing in a Subset of Retinal Bipolar Cells.

The retina and the olfactory bulb are the gateways to the visual and olfactory systems, respectively, similarly using neural networks to initiate sensory signal processing. Sensory receptors receive signals that are transmitted to neural networks before projecting to primary cortices. These networks filter sensory signals based on their unique features and adjust their sensitivities by gain control systems. Interestingly, dopamine modulates sensory signal transduction in both systems. In the retina, dopamine adjusts the retinal network for daylight conditions (“light adaptation”). In the olfactory system, dopamine mediates lateral inhibition between the glomeruli, resulting in odorant signal decorrelation and discrimination. While dopamine is essential for signal discrimination in the olfactory system, it is not understood whether dopamine has similar roles in visual signal processing in the retina. To elucidate dopaminergic effects on visual processing, we conducted patch-clamp recording from second-order retinal bipolar cells, which exhibit multiple types that can convey different temporal features of light. We recorded excitatory postsynaptic potentials (EPSPs) evoked by various frequencies of sinusoidal light in the absence and presence of a dopamine receptor 1 (D1R) agonist or antagonist. Application of a D1R agonist, SKF-38393, shifted the peak temporal responses toward higher frequencies in a subset of bipolar cells. In contrast, a D1R antagonist, SCH-23390, reversed the effects of SKF on these types of bipolar cells. To examine the mechanism of dopaminergic modulation, we recorded voltage-gated currents, hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, and low-voltage activated (LVA) Ca2+ channels. SKF modulated HCN and LVA currents, suggesting that these channels are the target of D1R signaling to modulate visual signaling in these bipolar cells. Taken together, we found that dopamine modulates the temporal tuning of a subset of retinal bipolar cells. Consequently, we determined that dopamine plays a role in visual signal processing, which is similar to its role in signal decorrelation in the olfactory bulb.

Effect of Decorrelation Factors on the Error in Measuring the Signal Phase Difference with Interferometric Systems

Based on the general model of signals scattered by the rough surface of the seabed, relations are developed for estimating the errors of measurement by the interferometric side-scan sonar (ISSS) of the arrival angles of echo signals caused by the decorrelation of the probing signals with a large base in the receiving channels due to the spatial separation of the antennas. The obtained relations allow us to adjust the signal processing algorithms received by the multiantenna interferometer to reduce the influence of additive noise on the accuracy of the measurements of the angles of arrival of echo signals.

Improving Ultrasound Lateral Strain Estimation Accuracy using Log Compression of Regularized Correlation Function.

Normalized cross-correlation (NCC) function used in ultrasound strain imaging can get corrupted due to signal decorrelation inducing large displacement errors. Bayesian regularization has been applied in an iterative manner to regularize the NCC function and to reduce estimation variance and peak-hopping errors. However, incorrect choice of the number of iterations can lead to over-regularization errors. In this paper, we propose the use of log compression of regularized NCC function to improve subsample estimation. Performance of parabolic interpolation before and after log compression of the regularized NCC function were compared in numerical simulations of uniform and inclusion phantoms. Significant improvement was achieved with the proposed scheme for lateral estimation results. For example, lateral signal-to-noise ratio (SNR) was 10 dB higher after log compression at 3% strain in a uniform phantom. Lateral contrast-to-noise ratio (CNR) was 1.81 dB higher with proposed method at 3% strain in inclusion phantom. No significant difference was observed in axial estimation due to presence of phase information and high sampling frequency. Our results suggest that this simple approach makes Bayesian regularization robust to over-regularization artifacts.

Landslide detection in mountainous forest areas using polarimetry and interferometric coherence

The cloud-free, wide-swath, day-and-night observation capability of synthetic aperture radar (SAR) has an important role in rapid landslide monitoring to reduce economic and human losses. Although interferometric SAR (InSAR) analysis is widely used to monitor landslides, it is difficult to use that for rapid landslide detection in mountainous forest areas because of significant decorrelation. We combined polarimetric SAR (PolSAR), InSAR, and digital elevation model (DEM) analysis to detect landslides induced by the July 2017 Heavy Rain in Northern Kyushu and by the 2018 Hokkaido Eastern Iburi Earthquake. This study uses fully polarimetric L-band SAR data from the ALOS-2 PALSAR-2 satellite. The simple thresholding of polarimetric parameters (alpha angle and Pauli components) was found to be effective. The study also found that supervised classification using PolSAR, InSAR, and DEM parameters provided high accuracy, although this method should be used carefully because its accuracy depends on the geological characteristics of the training data. Regarding polarimetric configurations, at least dual-polarimetry (e.g., HH and HV) is required for landslide detection, and quad-polarimetry is recommended. These results demonstrate the feasibility of rapid landslide detection using L-band SAR images.

Noise and bias in optical coherence tomography intensity signal decorrelation.

Functional optical coherence tomography (OCT) imaging based on the decorrelation of the intensity signal has been used extensively in angiography and is finding use in flowmetry and therapy monitoring. In this work, we present a rigorous analysis of the autocorrelation function, introduce the concepts of contrast bias, statistical bias and variability, and identify the optimal definition of the second-order autocorrelation function (ACF) g (2) to improve its estimation from limited data. We benchmark different averaging strategies in reducing statistical bias and variability. We also developed an analytical correction for the noise contributions to the decorrelation of the ACF in OCT that extends the signal-to-noise ratio range in which ACF analysis can be used. We demonstrate the use of all the tools developed in the experimental determination of the lateral speckle size depth dependence in a rotational endoscopic probe with low NA, and we show the ability to more accurately determine the rotational speed of an endoscopic probe to implement NURD detection. We finally present g (2)-based angiography of the finger nailbed, demonstrating the improved results from noise correction and the optimal bias mitigation strategies.