Incoherent Averaging Research Articles

Ionospheric Effects in GNSS-Reflectometry From Space

Global navigation satellite systems-reflectometry (GNSS-R) is an emerging technique that uses navigation opportunistic signals as a multistatic radar. Most GNSS systems operate at L-band, which is affected by the ionosphere. At present, there is only a GNSS-R space-borne scatterometer on board the UK TechDemoSat-1, but in late 2016, NASA will launch the CYGNSS constellation, and in 2019, ESA will carry out the GEROS experiment on board the International Space Station. In GNSS-R, reflected signals are typically processed in open loop using a short coherent integration time (~1 ms), followed by long incoherent averaging (~1000 times, ~1 s) to increase the signal-to-noise ratio. In this study, the global ionospheric scintillation model is first used to evaluate the total electron content and the scintillation index S4. The ionospheric scintillation impact is then evaluated as a degradation of the signal-to-noise ratio, which can be used to assess the altimetry and scatterometry performance degradation in a generic GNSS-R mission. Since ionospheric scintillations are mostly produced by a layer of electron density irregularities at ~350 km height, underneath most LEO satellites, but closer to them than to the Earth's surface, intensity scintillations occur especially in the GNSS transmitter-to-ground transect, therefore, the impact is very similar in conventional and interferometric GNSS-R. Using UK TechDemoSat-1 data, signal-to-noise ratio fluctuations are computed and geo-located, finding that they occur in the open ocean along ~±20° from the geomagnetic equator where S4 exhibits a maximum, and in low wind speed regions, where reflected signals contain a non-negligible coherent component.

A Statistical Model and Simulator for Ocean-Reflected GNSS Signals

Global Navigation Satellite Systems Reflectometry (GNSS-R) methods sense ocean roughness by cross correlating scattered GNSS signals with a locally generated replica of the transmitted signal. The resulting delay-Doppler map (DDM) is related to surface slope statistics through established scattering models. DDM samples are correlated in time and between delay and Doppler coordinates, limiting the number of independent samples available to reduce measurement error. Performance predictions for future GNSS-R missions depend on a model with sufficient fidelity to represent these statistics. A previously developed model for the correlation in time and a new model for the correlation between delays are used to create a GNSS-R signal simulator. A change of variables reduces these models to the numerically efficient form of a single integral and a convolution. Independent normally distributed white noise is passed through a filter bank implementing these models to generate an ensemble of synthetic noisy measurements having realistic correlation in time and between delay bins. Correlation between Doppler bins, however, is not represented by this model. The output of this simulator is compared to 1-D (delay-only) DDMs collected during a 2009 airborne experiment in the North Atlantic, with winds from 5 to 25 m/s. Good agreement is found in the variance, time correlation, and covariance matrix. The probability density functions show reasonable agreement. A bias between the synthetic and observed data was found to result from a bias in the wind/roughness retrieval. Agreement was worse for the low-wind (5.8 m/s) example, perhaps due to a component of specular reflection. One application of this simulator is in generating synthetic DDMs, maintaining accurate representation of statistics following nonlinear processing (e.g., incoherent averaging). The simulator presents a numerically efficient method for generating large statistically significant ensembles of DDMs under identical conditions.

GNSS-R Wind-Dependent Polarimetric Signature Over the Ocean

Global Navigation Satellite System-Reflectometry (GNSS-R) is a promising and innovative remote sensing technique. In this letter, an investigation on the polarimetric characteristics of GNSS-R signals is performed. The study is conducted by means of an ad hoc simulator based on a facet approach to approximate a 3-D time-domain generated sea surface. To best mimic a realistic configuration, the simulator uses both recorded GPS and Galileo signals as incident waves. The effect of the incoherent averaging in mitigating the speckle is shown. Finally, a relationship between the wind speed and the polarimetric ratio is analyzed.

Incoherent averaging of phase singularities in speckle-shearing interferometry.

Interferometric speckle techniques are plagued by the omnipresence of phase singularities, impairing the phase unwrapping process. To reduce the number of phase singularities by physical means, an incoherent averaging of multiple speckle fields may be applied. It turns out, however, that the results may strongly deviate from the expected √N behavior. Using speckle-shearing interferometry as an example, we investigate the mechanism behind the reduction of phase singularities, both by calculations and by computer simulations. Key to an understanding of the reduction mechanism during incoherent averaging is the representation of the physical averaging process in terms of certain vector fields associated with each speckle field.

Impacts of Ionospheric Scintillation on the BIOMASS P-Band Satellite SAR

The European Space Agency is conducting studies for a low-earth orbiting polarimetric synthetic aperture radar called BIOMASS to provide global measurements of forest biomass and tree height. Phase scintillation across the synthetic aperture caused by ionospheric irregularities can degrade the impulse response function (IRF) and cause squinting, and its temporal variation can cause decorrelation in repeat-pass interferometry. These effects are simulated for a range of conditions for the baseline BIOMASS system configuration using the Wideband model of scintillation, which predicts that for a dawn-dusk orbit, impacts of scintillation over forest regions are negligible under all conditions except at high latitudes in the North American sector under high sunspot activity. In this sector, single-look IRFs have mean integrated sidelobe ratios (ISLRs) and peak sidelobe ratios (PSLRs) better than 0 and -5 dB, respectively, at 90% confidence interval under median solar activity up to the northern tree line ( ~ 70° geomagnetic). Degradation in the mean 3-dB resolution of up to 10% is predicted, with mean absolute azimuth shifts of the IRF peak of up to 2 m, which increases to 5 m at high sunspot number. Similar values are found for the dawn and dusk sides, and seasonal variations are negligible for latitudes below the tree line. Repeat-pass interferometric image pairs maintain coherence up to 50 ° N under median sunspot conditions. Four-look processing improves the ISLR and PSLR by several decibels, but causes significant degradation of the 3-dB resolution due to incoherent averaging of images with different random azimuth shifts.

Application of special-purpose artificial neural networks for speckle reduction in SAR images

Synthetic aperture radar (SAR) is used extensively for remote-sensing applications due to its ability to operate under all weather conditions and provide high-resolution images. However, high-resolution images constructed from SAR data often suffer from speckle, which makes identification and classification of edges/boundaries a difficult task. Speckle noise is multiplicative in nature and is a result of constructive and destructive interference of signals from randomly distributed scatterers in a resolution cell illuminated by a coherent signal. Usually, speckle is reduced by incoherent averaging of high-resolution image pixels that degrade resolution. The principal goal in all speckle-reduction algorithms is to reduce speckle with minimum loss of resolution. In this investigation, we used specially trained and validated artificial neural networks (ANNs) for speckle reduction in images generated with a radar-depth sounder/imager and compared their performance to the conventional adaptive filtering and Speckle Reducing Anisotropic Diffusion (SRAD) algorithm. We show that by training different ANNs to reduce speckle noise at different levels of signal-to-noise ratio (SNR), rather than training one ANN to operate at all levels of SNR, improved performance in speckle reduction can be obtained. Real SAR images and synthetic noise are used in this research to compare the performance of the proposed ANN-based approaches with that obtained from conventional methods. This investigation shows that on combining the results from a set of properly trained and validated neural networks, the SNRs of the output images improve beyond those obtained from conventional approaches when the input SNRs are greater than or equal to 4 dB. For input SNRs greater than 0 dB, however, the ANNs provide better performance in edge preservation compared with conventional methods. We also found that once a set of ANNs is properly trained to reduce speckle from an image, these ANNs can be used in de-speckling other images without any further training. The merits and demerits of different configurations of the ANNs are studied to find useful speckle noise-tolerant ANN architectures.

Coherent total internal reflection dark-field microscopy: label-free imaging beyond the diffraction limit

Coherent imaging is barely applicable in life-science microscopy due to multiple interference artifacts. Here, we show how these interferences can be used to improve image resolution and contrast. We present a dark-field microscopy technique with evanescent illumination via total internal reflection that delivers high-contrast images of coherently scattering samples. By incoherent averaging of multiple coherent images illuminated from different directions we can resolve image structures that remain unresolved by conventional (incoherent) fluorescence microscopy. We provide images of 190nm beads revealing resolution beyond the diffraction limit and slightly increased object distances. An analytical model is introduced that accounts for the observed effects and which is confirmed by numerical simulations. Our approach may be a route to fast, label-free, super-resolution imaging in live-cell microscopy.

Non‐water‐suppressed proton MR spectroscopy improves spectral quality in the human spinal cord

Magnetic resonance spectroscopy enables insight into the chemical composition of spinal cord tissue. However, spinal cord magnetic resonance spectroscopy has rarely been applied in clinical work due to technical challenges, including strong susceptibility changes in the region and the small cord diameter, which distort the lineshape and limit the attainable signal to noise ratio. Hence, extensive signal averaging is required, which increases the likelihood of static magnetic field changes caused by subject motion (respiration, swallowing), cord motion, and scanner-induced frequency drift. To avoid incoherent signal averaging, it would be ideal to perform frequency alignment of individual free induction decays before averaging. Unfortunately, this is not possible due to the low signal to noise ratio of the metabolite peaks. In this article, frequency alignment of individual free induction decays is demonstrated to improve spectral quality by using the high signal to noise ratio water peak from non-water-suppressed proton magnetic resonance spectroscopy via the metabolite cycling technique. Electrocardiography (ECG)-triggered point resolved spectroscopy (PRESS) localization was used for data acquisition with metabolite cycling or water suppression for comparison. A significant improvement in the signal to noise ratio and decrease of the Cramér Rao lower bounds of all metabolites is attained by using metabolite cycling together with frequency alignment, as compared to water-suppressed spectra, in 13 healthy volunteers.

A novel phase and frequency navigator for proton magnetic resonance spectroscopy using water-suppression cycling.

Magnetic resonance spectroscopy is sensitive to movements, in part, because of motion-induced phase and frequency variations that lead to incoherent averaging. For in vivo proton magnetic resonance spectroscopy, the unsuppressed or under-suppressed water signal can be used to restore coherent averaging; however, this approach results in baseline distortions due to the large water peak. Therefore, a novel water-suppression cycling scheme was developed that alternates between positive and negative residual water signal. Using the residual water signal, the method allows for shot-to-shot phase and frequency correction of individual free induction decays and restoration of signal losses due to incoherent averaging, yet near-complete elimination of residual water. It is demonstrated that the residual water signal can be used to restore metabolite peaks in a brain spectrum from a subject who performed intentional head movements. The ability to correct phase and frequency fluctuations during subject motion is vital for use with adaptive motion correction approaches that ensure proper voxel positioning during head movements.

Effect of multiple scattering and modal dispersion on population density imagery of Atlantic herring shoals with ocean acoustic waveguide remote sensing.

Wide area sonar such as ocean acoustic waveguide remote sensing (OAWRS) has been shown to be a useful tool for instantaneously imaging large shoals of fish distributed over continental shelf regions. Here, we show that the population density of fish groups can be accurately estimated by using incoherent averaging of the matched filtered returns. Numerical Monte-Carlo models are applied to simulate the active imaging system and determine the statistics of the received matched filtered intensity scattered off Atlantic herring shoals in the Gulf of Maine. The model includes multiple scattering of the dense fish groups and uses a range-dependent acoustic model to simulate modal dispersion in the fluctuating ocean environment. We illustrate the effects of modal dispersion on the sonar imagery for various seafloor types. We determine the conditions for when multiple scattering is significant for Atlantic herring shoals, which depends on the fish density, imaging frequency, and mean target strength. Results of the model are compared to OAWRS sonar imagery collected during the 2006 Gulf of Maine Experiment.

Cryo-Electron Microscopy Structure of an Adenovirus-Integrin Complex Indicates Conformational Changes in both Penton Base and Integrin

A structure of adenovirus type 12 (HAdV12) complexed with a soluble form of integrin alphavbeta5 was determined by cryo-electron microscopy (cryoEM) image reconstruction. Subnanometer resolution (8 A) was achieved for the icosahedral capsid with moderate resolution (27 A) for integrin density above each penton base. Modeling with alphavbeta3 and alpha(IIb)beta3 crystal structures indicates that a maximum of four integrins fit over the pentameric penton base. The close spacing (approximately 60 A) of the RGD protrusions on penton base precludes integrin binding in the same orientation to neighboring RGD sites. Flexible penton-base RGD loops and incoherent averaging of bound integrin molecules explain the moderate resolution observed for the integrin density. A model with four integrins bound to a penton base suggests that integrin might extend one RGD-loop in the direction that could induce a conformational change in the penton base involving clockwise untwisting of the pentamer. A global conformational change in penton base could be one step on the way to the release of Ad vertex proteins during cell entry. Comparison of the cryoEM structure with bent and extended models for the integrin ectodomain reveals that integrin adopts an extended conformation when bound to the Ad penton base, a multivalent viral ligand. These findings shed further light on the structural basis of integrin binding to biologically relevant ligands, as well as on the molecular events leading to HAdV cell entry.

LOAPEX: The Long-Range Ocean Acoustic Propagation EXperiment

This paper provides an overview of the experimental goals and methods of the Long-range Ocean Acoustic Propagation EXperiment (LOAPEX), which took place in the northeast Pacific Ocean between September 10, 2004 and October 10, 2004. This experiment was designed to address a number of unresolved issues in long-range, deep-water acoustic propagation including the effect of ocean fluctuations such as internal waves on acoustic signal coherence, and the scattering of low-frequency sound, in particular, scattering into the deep acoustic shadow zone. Broadband acoustic transmissions centered near 75 Hz were made from various depths to a pair of vertical hydrophone arrays covering 3500 m of the water column, and to several bottom-mounted horizontal line arrays distributed throughout the northeast Pacific Ocean Basin. Path lengths varied from 50 km to several megameters. Beamformed receptions on the horizontal arrays contained 10-20-ms tidal signals, in agreement with a tidal model. Fifteen consecutive receptions on one of the vertical line arrays with a source range of 3200 km showed the potential for incoherent averaging. Finally, shadow zone receptions were observed on an ocean bottom seismometer at a depth of 5000 m from a source at 3200-250-km range.

Poynoret al.Reply:

The author replies to comments on his original article. The abstract from the original item is included here. Synchrotron x-ray reflectivity measurements of the interface between water and methyl-terminated octadecylsilane monolayers with stable contact angle >100{sup o} conclusively show a depletion layer, whether or not the water is degassed. The thickness is of order one water molecule: 2-4 {angstrom} with electron density <40% that of bulk water. Considerations of coherent and incoherent averaging of lateral inhomogeneities show that the data cannot be explained by 'nanobubbles'. When the contact angle is lower, unstable in time, or when monolayers fail to be sufficiently smooth over the footprint of the x-ray beam, there is no recognizable depletion.

Extending the Dynamic Range of a Spectrum Monitor Using Comparison Switching and Spectral Averaging

The dynamic range of a spectrum monitor can be extended by incoherently averaging the difference between the spectrum from the antenna and the spectrum from a comparison load. We show that it is often advantageous to increase the attenuation to the spectrum analyzer, thereby lowering the maximum signal strength at the mixer and subsequently compensating for the added noise by incoherent spectral averaging. We analyzed the case of a conventional heterodyne analyzer as well as an analyzer using direct analog-to-digital conversion (ADC) sampling.

Spectroscopic imaging with volume selection by unpaired adiabatic π pulses: Theory and application

In NMR spectroscopy, volume selection can be advantageously achieved using adiabatic π pulses, which enable high bandwidth and B 1 insensitivity. In order to avoid the generation of non-linear phase profiles and the subsequent signal loss caused by incoherent averaging, adiabatic π pulses are usually used in pairs for volume selection in each spatial dimension. Alternatively, when performing spectroscopic imaging (SI), a high enough spatial resolution results in negligible phase dispersion within each pixel. This allows using only one pulse per selected spatial dimension, resulting in a reduced echo-time and reduced power deposition. In this work, the feasibility of such an approach is explored theoretically and numerically, allowing the derivation of explicit conditions to obtain SI images without artifact. Adequate spatial and spectral post-processing procedures are described to compensate for the effect of non-linear phase profiles. These developments are applied to SI in the rat brain at 9.4 T, using a new adiabatic sequence named Pseudo-LASER.

Exploiting Phase Fluctuations to Improve Machine Performance Monitoring

Machines are an integral and important part of modern life. Machine condition monitoring is of vital importance to modern industry in its quest for higher reliability, quality, and efficiency. A new signal-processing technique for machine performance monitoring is presented. This new technique exploits fluctuations in phase angles of machine rotational frequency signals to determine their dynamic temporal coherence. Temporal coherence is the key to automatically identify a fault condition and assess its severity. The exploitation of temporal coherence also provides increased spectral resolution and signal-to-noise ratio (SNR). Tests were conducted on an edger trimmer ball bearing assembly that was subjected to different levels of fault conditions, such as a hairline crack on the outer race and sand contamination in the bearing. An electric fan motor with bearing faults was also tested. The fault identification capabilities of incoherent power averaging are compared with those of the new coherence processors. Some rotational signals could not be identified in the average power spectra and, therefore, the average power could not be used for monitoring these signals. However, they were easily identified in the spectra of the new processors, with their increased SNR gain, and were used successfully for machine performance monitoring and diagnostics. This new processing capability for quantifying and exploiting the dynamic temporal coherences of a machine's signals provides a valuable capability for detecting existing and developing faults, and for monitoring their progress. This is also true during the startup and shutdown phases of machines, when their speeds and corresponding rotational frequencies are changing. Note to Practitioners - General guidance for monitoring a machine's performance is included below. Calculate the coherence parameter phi and establish the base-line temporal coherences of a machine operating in good condition for future reference, or for a similar machine that is known to be in good operating condition. Continuously monitor the machine's rotational signals via accelerometer or microphone, measuring the temporal coherences using the FFT-derived coherence parameter phi. Calculate/display the temporal coherence time histories of phi for the rotational signals. Analyze the phi coherence patterns. A constant thickness, continuously nonrandom, phi temporal coherence time history pattern, indicates fault-free machine operation. A decreasing spread in the phi time history pattern with time indicates increasing temporal coherence of a rotational frequency component due to a mechanical fault. The frequency indicates which machine component is faulty. Possible problem: a fault associated with a bearing (e.g., a crack in a bearing race). A broadening phi time history pattern of a rotational frequency component indicates decreasing temporal coherence. Possible problems: need for maintenance, worn out lubrication, or foreign matter (dust, grit, sand) in the bearing

How Water Meets a Hydrophobic Surface

Synchrotron x-ray reflectivity measurements of the interface between water and methyl-terminated octadecylsilane monolayers with stable contact angle >100 degrees conclusively show a depletion layer, whether or not the water is degassed. The thickness is of order one water molecule: 2-4 Angstrom with electron density <40% that of bulk water. Considerations of coherent and incoherent averaging of lateral inhomogeneities show that the data cannot be explained by "nanobubbles." When the contact angle is lower, unstable in time, or when monolayers fail to be sufficiently smooth over the footprint of the x-ray beam, there is no recognizable depletion.

Quasiclassical calculation of the quasiparticle thermal conductivity in a mixed state

We report the result of calculation of the quasiparticle thermal conductivity κ xx ( ∇ T ⊥ B ) in the vortex state of a two-dimensional superconductor. We compute κ xx for both s-wave and d-wave superconductors, taking account of the spatial dependence of normal Green's function g, which is neglected in the previous studies using the Brandt–Pesch–Tewordt (BPT) method. Our results indicate that κ xx based on the BPT method is slightly underestimated due to its incoherent spatial averaging procedure.

Speckle Reduction in OCT using Massively-Parallel Detection and Frequency-Domain Ranging

Speckle noise significantly limits the information content provided by coherent optical imaging methods such as optical coherence tomography and its recent derivative, optical frequency-domain imaging (OFDI). In this paper, we demonstrate a novel OFDI system that simultaneously acquires hundreds of angularly resolved images, which can be compounded to reduce speckle noise. The system comprises an InGaAs line-scan camera and an interferometer, configured so that the elements of the detector array simultaneously capture light spanning a backscattering angular range of 32 degrees. On successive read-outs of the array, the wavelength of the laser source was stepped through a range of 130 nm centered at 1295 nm to concurrently generate 400 angle-resolved OFDI images. A theory of angle-resolved OFDI and the design equations of the system are presented. Incoherent averaging of the angle-resolved data is shown to yield substantial speckle reduction (as high as an 8 dB SNR improvement) in images of a tissue phantom and esophageal tissue ex vivo.

Coherent x-ray diffraction from quantum dots

Coherent x-ray diffraction is a new experimental method for studying perfect and imperfect crystals. Instead of incoherent averaging, a coherent sum of amplitudes produces a coherent diffraction pattern originating from the real space arrangement of the sample. We applied this method for studying quantum dot samples that were specially fabricated GeSi islands of nanometer size and in a regular array embedded into a Si substrate. A coherent beam was focused by special Kirkpatric--Baez optics to a micrometer size. In the experiment it was observed that such a microfocused coherent beam produced coherent diffraction pattern with Bragg spots and broad diffuse maxima. The diffuse peak breaks up into a fine speckle pattern. The grazing incidence diffraction pattern has a typical shape resulting from the periodic array of identical islands. We used this diffraction pattern to reconstruct the average shape of the islands using a model independent approach.