Doppler Velocity Measurements Research Articles

Nonthermal Velocities in a Solar Active Region Observed by SERTS

Abstract We present measurements of coronal nonthermal Doppler velocities in NOAA Active Region 7870 observed by the Solar EUV Research Telescope and Spectrograph during its 1995 sounding rocket flight (SERTS-95). The instrument included a multilayer-coated toroidal diffraction grating that enhanced its sensitivity in second order at wavelengths between about 171 and 225 Å. Analyses of spectra from this flight were previously published but did not address the issue of nonthermal velocities; we do so here for spectra averaged over the 123 . ″ 8 segment of the slit for which the brightest portion of the region observed by SERTS was recorded. All emission lines are fit with Gaussian profiles. With post-flight laboratory spectra of He ii and Ne II-IIINe we derive a first-order instrumental width (full width at half-maximum intensity) of F inst ,1 = 50.6 ± 3.1 mÅ and a second-order instrumental width of F inst ,2 = 25.3 ±1.5 mÅ. For emission lines of Fe x−Fe xv, formed at 6.05 ≤ log T(K) ≤ 6.35, we find nonthermal velocities between 26.1 and 35.0 km s−1, independent of temperature; this range corresponds to V nonth = 30.6 ± 4.5 km s−1. For the two unblended Fe xii lines at 192.394 and 193.509 Å closest to the maximum sensitivity of SERTS-95, we have V nonth = 33.7 ± 1.3 km s−1. When solar observations are used to derive or confirm instrumental widths for spectrometers in orbit, those observations should compensate for nonthermal velocities such as the ones reported here in order to extract instrumental widths that are free of nonthermal broadening.

Chaff Cloud Integrated Communication and TT&C: An Integrated Solution for Single-Station Emergency Communications and TT&C in a Denied Environment

In response to potential denial environments such as canyons, gullies, islands, and cities where users are located, traditional Telemetry, Tracking, and Command (TT&C) systems can still maintain core requirements such as availability, reliability, and sustainability in the face of complex electromagnetic environments and non-line-of-sight environments that may cause service degradation or even failure. This paper presents a single-station emergency solution that integrates communication and TT&C (IC&T) functions based on radar chaff cloud technology. Firstly, a suitable selection of frequency bands and modulation methods is provided for the emergency IC&T system to ensure compatibility with existing communication and TT&C systems while catering to the future needs of IC&T. Subsequently, theoretical analyses are conducted on the communication link transmission loss, data transmission, code tracking accuracy, and anti-multipath model of the emergency IC&T system based on the chosen frequency band and modulation mode. This paper proposes a dual-way asynchronous precision ranging and time synchronization (DWAPR&TS) system employing dual one-way ranging (DOWR) measurement, a dual-way asynchronous incoherent Doppler velocity measurement (DWAIDVM) system, and a single baseline angle measurement system. Next, we analyze the physical characteristics of the radar chaff and establish a dynamic model of spherical chaff cloud clusters based on free diffusion. Additionally, we provide the optimal strategy for deploying chaff cloud. Finally, the emergency IC&T application based on the radar chaff cloud relay is simulated, and the results show that for severe interference, taking drones as an example, under a measurement baseline of 100 km, the emergency IC&T solution proposed in this paper can achieve an accuracy range of approximately 100 m, a velocity accuracy of 0.1 m/s, and an angle accuracy of 0.1°. In comparison with existing TT&C system solutions, the proposed system possesses unique and potential advantages that the others do not have. It can serve as an emergency IC&T reference solution in denial environments, offering significant value for both civilian and military applications.

Mitigation method of acoustic doppler velocity measurement bias

A Doppler velocity log (DVL) is used to estimate ship’s velocity by processing seafloor echo. The velocity measurement bias (i.e., long term accuracy) is often regarded as an important technical specification to quantify the accuracy of DVL. The bias describes the accuracy of the instrument after averaging over a sufficiently long time, such that the velocity variance approaches zero. The goal of this paper is to obtain an effective method to mitigate the bias in velocity measurement based on analyzing the DVL seafloor echo characteristics. First, a DVL bottom echo model is constructed based on highlight model, which can focus on the statistical characteristics of the echo waveform information. Then the algorithm for retrieving the echo highlight information is presented. The parameters of the highlight model based on experimental data of DVL echo are analyzed. The intrinsic connection between the highlight model and the point-based scattering model is discussed. Finally, the method of velocity measurement to mitigate the bias according to the relationship between the statistical characteristics of the highlights and bias is put forward. Simulation results show that the proposed method significantly mitigates the bias.

Incorporating Doppler velocity measurements in orientation-based extended object tracking

Incorporating Doppler velocity measurements in orientation-based extended object tracking

Spectroscopic Observations of Coronal Rain Formation and Evolution Following an X2 Solar Flare

A significant impediment to solving the coronal heating problem is that we currently only observe active region loops in their cooling phase. Previous studies showed that the evolution of cooling loop densities and apex temperatures is insensitive to the magnitude, duration, and location of energy deposition. Still, potential clues to how energy is released are encoded in the properties of the cooling phase. The appearance of coronal rain, one of the most spectacular phenomena of the cooling phase, occurs when plasma has cooled below 1 MK, which sets constraints on the heating frequency, for example. Most observations of coronal rain have been made by imaging instruments. Here we report rare Hinode/EUV Imaging Spectrometer (EIS) observations of a loop arcade where coronal rain forms following an X2.1 limb flare. A bifurcation in plasma composition measurements between photospheric at 1.5 MK and coronal at 3.5 MK suggests that we are observing postflare-driven coronal rain. Increases in nonthermal velocities and densities with decreasing temperature (2.7–0.6 MK) suggest that we are observing the formation and subsequent evolution of the condensations. Doppler velocity measurements imply that a 10% correction of apparent flows in imaging data is reasonable. Emission measure analysis at 0.7 MK shows narrow temperature distributions, indicating coherent behavior reminiscent of that observed in coronal loops. The limitations on spatio-temporal resolution of EIS suggest that we are observing the largest features or rain showers. These observations provide insights into the heating rate, source, turbulence, and collective behavior of coronal rain from observations of the loop cooling phase.

Development of an in vitro platform for transesophageal echocardiographic and color Doppler hemodynamic assessments of left atrial appendage occlusion devices

Abstract Introduction Device-related thrombus (DRT) remains a significant complication after left atrial appendage occlusion (LAAO) and is associated with device position. Clinical prospective study of the influence of device position on DRT is cumbersome. Purpose To establish an in vitro bench test platform that allows visual, transesophageal echocardiographic and color Doppler hemodynamic assessments of left atrial appendage occlusion devices. Methods Based on a mock circulatory system, an in vitro platform was developed to create pathophysiological environment and left atrium wall motion under atrial fibrillation condition. Results LAAO devices could be visualized in the left atrium phantom model using transesophageal echocardiography and color Doppler measurement of local blood velocity at LAAO devices were proven to be capable. Deep implantations resulted in uncovered left upper pulmonary vein ridges. In comparison to ostium-fitted device positions, deep implantations were associated with reduced velocity of blood and the local velocity in the "cul-de-sac" remained lower than the critical threshold of 0.2 m/s. Conclusions An in vitro platform was developed that allows transesophageal echocardiographic and color Doppler hemodynamic assessments of left atrial appendage occlusion devices.

Ultrahigh Precision Angular Velocity Measurement using Frequency Shift of Partially Coherent Beams

AbstractIn the recent decade, the rotational Doppler effect has garnered considerable attention for stimulating the development of applications such as rotational Doppler velocity and topological charge measurements. Previous studies performed measurements under sources with one or multiple amplitude, phase, and polarization modulations. However, the applicability of these schemes is limited by the crucial factor of alignment between the source and object, especially if the magnitude of the source is greater than the object size. Therefore, this study proposes a partially coherent angular velocity measurement model that allows the rotational axes of targets to deviate from the source center and is even less susceptible to external jitters. Accordingly, a proof‐of‐principle experiment to determine the angular velocity under arbitrary alignment conditions is conducted. Tracing the rotational motion by rotating the coherent structure of the source results in a frequency shift—red shift for the same rotation and blue shift for a reverse rotation. The angular velocity vectors (both magnitude and direction) of two anisotropic sub‐Rayleigh objects are successfully measured with ultrahigh precision. The lowest angular velocity is 0.001 r s−1. The average relative error is less than 0.05% with sufficient sampling. Thus, the present findings can be applied to velocity metrology and micromanipulation.

The classification of atmospheric hydrometeors and aerosols from the EarthCARE radar and lidar: the A-TC, C-TC and AC-TC products

Abstract. The EarthCARE mission aims to probe the Earth's atmosphere by measuring cloud and aerosol profiles using its active instruments, the Cloud Profiling Radar (CPR) and ATmospheric LIDar (ATLID). The correct identification of hydrometeors and aerosols from atmospheric profiles is an important step in retrieving the properties of clouds, aerosols and precipitation. Ambiguities in the nature of atmospheric targets can be removed using the synergy of collocated radar and lidar measurements, which is based on the complementary spectral response of radar and lidar relative to atmospheric targets present in the profiles. The instruments are sensitive to different parts of the particle size distribution and provide independent but overlapping information in optical and microwave wavelengths. ATLID is sensitive to aerosols and small cloud particles, and CPR is sensitive to large ice particles, snowflakes and raindrops. It is therefore possible to better classify atmospheric targets when collocated radar and lidar measurements exist compared to using a single instrument. The cloud phase, precipitation and aerosol type within the column sampled by the two instruments can then be identified. ATLID-CPR target classification (AC-TC) is the product created for this purpose by combining the ATLID target classification (A-TC) and CPR target classification (C-TC). AC-TC is crucial for the subsequent synergistic retrieval of cloud, aerosol and precipitation properties. AC-TC builds upon previous target classifications using CloudSat and CALIPSO synergy while providing richer target classification using the enhanced capabilities of EarthCARE's instruments, specifically CPR's Doppler velocity measurements to distinguish snow and rimed snow from ice clouds and ATLID's lidar ratio measurements to objectively discriminate between different aerosol species and optically thin ice clouds. In this paper, we first describe how the single-instrument A-TC and C-TC products are derived from ATLID and CPR measurements. Then the AC-TC product, which combines the A-TC and C-TC classifications using a synergistic decision matrix, is presented. Simulated EarthCARE observations based on combined cloud-resolving and aerosol model data are used to test the processors generating the target classifications. Finally, the target classifications are evaluated by quantifying the fractions of ice and snow, liquid clouds, rain, and aerosols in the atmosphere that can be successfully identified by each instrument and their synergy. We show that radar–lidar synergy helps better detect ice and snow, with ATLID detecting radiatively important optically thin cirrus and cloud tops, while CPR penetrates most deep and highly concentrated ice clouds. The detection of rain and drizzle is entirely due to C-TC, while that of liquid clouds and aerosols is due to A-TC. The evaluation also shows that simple assumptions can be made to compensate for when the instruments are obscured by extinction (ATLID) or surface clutter and multiple scattering (CPR); this allows for the recovery of the majority of liquid cloud not detected by the active instruments.

Doppler Light Detection and Ranging-Aided Inertial Navigation and Trajectory Recovery

A sensor fusion approach to terrain relative navigation is proposed in this paper. Sensor data from Global Position System (GPS), Inertial Navigation System (INS), and Doppler light detection and ranging (LIDAR) provide a relative state estimate through a novel multiplicative extended Kalman filter. The measurement model of the Doppler LIDAR is derived from first principals and employed by the filter to update the estimates propagated by the GPS/INS data stream. The line-of-sight Doppler velocity measurement provides direct feedback to the velocity states and improves sensitivity to these state estimates. A novel Rauch–Tung–Striebel smoother framework is derived that is compatible with states exhibiting multiplicative error that enables seamless postprocessing of state estimates filtered with a multiplicative extended Kalman filter. This new filter and smoother framework is tested with suborbital flight data from Blue Origin’s New Sheppard ascent and landing missions.

Development of a methodology for evaluating spaceborne W-band Doppler radar by combined use of Micro Rain Radar and a disdrometer in Antarctica

Ground-based snowfall observations over Antarctica are rare due to the harsh environment and high logistical, equipment maintenance, and operational costs. Satellite measurements are crucial to provide continent-wide precipitation estimates, and this highlights the importance of validating the satellite estimates with measurements collected by ground-based Antarctic stations. The NASA CloudSat satellite, launched in 2006, is equipped with a 94 GHz (W-band) Cloud Profiling Radar (CPR) that provides measurements of reflectivity profiles of clouds and precipitation, whereas the incoming ESA/JAXA EarthCARE mission will add Doppler capability to a 94 GHz radar. This study explores how the synergy between two instruments available at most Antarctic stations, i.e., Micro Rain Radar (24 GHz, K-band) and laser disdrometer, can be used to validate satellite-borne W-band radar measurements, including Doppler estimates. A new validation methodology (K2W) was proposed to combine these instruments for simulating the 94 GHz reflectivity and Doppler measurements from Micro Rain Radar spectra. Assessment of the proposed K2W conversion methodology showed that the CloudSat Ze profiles can be simulated by the method with 0.2 dB mean difference at the lowest satellite radar range bin when time lag within ±12.5 min and the distance within 25 km around the CloudSat overpass were considered. With the K2W method, the 94 GHz Doppler velocity below 1 km altitude that would be observed by EarthCARE was obtained, and the standard deviation of the simulated Doppler velocity was found to be smaller than about 0.2 m s−1. The simulated 94 GHz Doppler radar profile information, which is less affected by attenuation compared to ground-based 94 GHz radar, will significantly improve the quantification of precipitation over Antarctica. This methodology will be applied to further assess the EarthCARE CPR Doppler velocity measurement accuracy as well as the Level 2 standard products for precipitation in Antarctica and at many other ground observation sites.

Fetal pulmonary artery Doppler blood flow velocity measures and early infant lung function. A prospective cohort study

Background Reduced lung function at birth has evident antenatal origins and is associated with an increased risk of wheezing and asthma later in life. Little is known about whether blood flow in the fetal pulmonary artery, may impact postnatal lung function. Objective Our primary aim was to investigate the potential associations between fetal Doppler blood flow velocity measures in the fetal branch pulmonary artery, and infant lung function by tidal flow-volume (TFV) loops at three months of age in a low-risk population. Our secondary aim was to explore the association between Doppler blood flow velocity measures in the umbilical and middle cerebral arteries, and the same lung function measures. Methods In 256 non-selected pregnancies from the birth cohort study Preventing Atopic Dermatitis and ALLergies in Children (PreventADALL) we performed fetal ultrasound examination with Doppler blood flow velocity measurements at 30 gestational weeks (GW). We recorded the pulsatility index, peak systolic velocity, time-averaged maximum velocity, acceleration time/ejection time ratio, and time velocity integral primarily in the proximal pulmonary artery close to the pulmonary bifurcation. The pulsatility index was measured in the umbilical and middle cerebral arteries and the peak systolic velocity in the middle cerebral artery. The cerebro-placental ratio (ratio between pulsatility index in the middle cerebral and umbilical arteries) was calculated. Infant lung function was assessed using TFV loops in awake, calmly breathing three months old infants. The outcome was the time to peak tidal expiratory flow to expiratory time ratio (t PTEF/t E), t PTEF/t E <25th percentile, and tidal volume per kg body weight (V T/kg). Potential associations between fetal Doppler blood flow velocity measures and infant lung function were assessed using linear and logistic regressions. Results The infants were born at median (min - max) 40.3 (35.6 − 42.4) GW, with a mean (SD) birth weight of 3.52 (0.46) kg, and 49.4% were females. The mean (SD) t PTEF/t E was 0.39 (0.1) and the 25th percentile was 0.33. Neither univariable nor multivariable regression models revealed any associations between fetal pulmonary blood flow velocity measures and t PTEF/t E, t PTEF/t E <25th percentile, or V T/kg at three months of age. Similarly, we did not observe associations between Doppler blood flow velocity measures in the umbilical and middle cerebral arteries and infant lung function measures. Conclusion In a cohort of 256 infants from the general population, fetal third-trimester Doppler blood flow velocity measures in the branch pulmonary, umbilical, and middle cerebral arteries were not associated with infant lung function measures at three months of age.

Velocimeter Light-Detection-and-Ranging–Informed Terrain Relative Navigation

Velocimeter light-detection-and-ranging (LIDAR)–informed batch and sequential estimation approaches for terrain relative navigation applications are developed in this paper. State-of-the-art velocimeter LIDAR sensors are capable of delivering simultaneous three-dimensional relative position, relative Doppler velocity, and reflectivity measurements for every point in the field of view. The proposed batch estimation algorithm yields accurate and statistically consistent vehicle velocity estimates in unknown (uncooperative) environments. This work presents novel pointwise relative position and Doppler velocity measurement models that can be utilized in the presence of known (cooperative) environments. Rate estimates obtained by the batch estimator are fused with the pointwise measurement models and classical inertial measurement unit formulations in the form of a multiplicative extended Kalman filter. This Doppler LIDAR-based sensor fusion approach yields informative terrain relative navigation solutions suitable for both cooperative and uncooperative environments. Results from extensive emulation robotics testing performed at NASA Johnson Space Center and Texas A&M’s Land, Air, and Space Robotics laboratory are presented to validate the proposed methodologies.

Extreme Convective Rainfall and Flooding from Winter Season Extratropical Cyclones in the Mid-Atlantic Region of the United States

Abstract Extreme rainfall from extratropical cyclones and the distinctive hydrology of the winter season both contribute to flood extremes in the Mid-Atlantic region. In this study, we examine extreme rainfall and flooding from a winter season extratropical cyclone that passed through the eastern United States on 24/25 February 2016. Extreme rainfall rates during the 24/25 February 2016 time period were produced by supercell thunderstorms; we identify supercells through local maxima in azimuthal shear fields computed from Doppler velocity measurements from WSR-88D radars. Rainfall rates approaching 250 mm h−1 from a long-lived supercell in New Jersey were measured by a Parsivel disdrometer. A distinctive element of the storm environment for the 24/25 February 2016 storm was elevated values of convective available potential energy (CAPE). We also examine the climatology of atmospheric rivers (ARs)—like the February 2016 storm—based on an identification and tracking algorithm that uses Twentieth Century Reanalysis fields for the 66-yr period from 1950 to 2015. Climatological analyses suggest that AR frequency is increasing over the Mid-Atlantic region. An increase in AR frequency, combined with increasing frequency of elevated CAPE during the winter season over the Mid-Atlantic region, could result in striking changes to the climatology of extreme floods.

Automated multi-beat tissue Doppler echocardiography analysis using deep neural networks.

Tissue Doppler imaging is an essential echocardiographic technique for the non-invasive assessment of myocardial blood velocity. Image acquisition and interpretation are performed by trained operators who visually localise landmarks representing Doppler peak velocities. Current clinical guidelines recommend averaging measurements over several heartbeats. However, this manual process is both time-consuming and disruptive to workflow. An automated system for accurate beat isolation and landmark identification would be highly desirable. A dataset of tissue Doppler images was annotated by three cardiologist experts, providing a gold standard and allowing for observer variability comparisons. Deep neural networks were trained for fully automated predictions on multiple heartbeats and tested on tissue Doppler strips of arbitrary length. Automated measurements of peak Doppler velocities show good Bland-Altman agreement (average standard deviation of 0.40cm/s) with consensus expert values; less than the inter-observer variability (0.65cm/s). Performance is akin to individual experts (standard deviation of 0.40 to 0.75cm/s). Our approach allows for > 26 times as many heartbeats to be analysed, compared to a manual approach. The proposed automated models can accurately and reliably make measurements on tissue Doppler images spanning several heartbeats, with performance indistinguishable from that of human experts, but with significantly shorter processing time. HIGHLIGHTS: • Novel approach successfully identifies heartbeats from Tissue Doppler Images • Accurately measures peak velocities on several heartbeats • Framework is fast and can make predictions on arbitrary length images • Patient dataset and models made public for future benchmark studies.

Picking up Speed: Continuous-Time Lidar-Only Odometry Using Doppler Velocity Measurements

Frequency-Modulated Continuous-Wave (FMCW) lidar is a recently emerging technology that additionally enables per-return instantaneous relative radial velocity measurements via the Doppler effect. In this letter, we present the first continuous-time lidar-only odometry algorithm using these Doppler velocity measurements from an FMCW lidar to aid odometry in geometrically degenerate environments. We apply an existing continuous-time framework that efficiently estimates the vehicle trajectory using Gaussian process regression to compensate for motion distortion due to the scanning-while-moving nature of any mechanically actuated lidar (FMCW and non-FMCW). We evaluate our proposed algorithm on several real-world datasets, including publicly available ones and datasets we collected. Our algorithm outperforms the only existing method that also uses Doppler velocity measurements, and we study difficult conditions where including this extra information greatly improves performance. We additionally demonstrate state-of-the-art performance of lidar-only odometry with and without using Doppler velocity measurements in nominal conditions. Code for this project can be found at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/utiasASRL/steam_icp</uri> .

Estimation of Doppler Velocity Degradation Due to Difference in Beam-Pointing Directions

This study investigated the effects of the difference of beam-pointing directions on the Doppler measurements from a spaceborne Ku-band precipitation radar. The Doppler measurement system was a displaced phase center antenna (DPCA) using two antennas that are displaced in the along-track direction. When the beam-pointing directions of the two antennas deviate, Doppler measurements can degrade. The well-known formula for the uncertainty in Doppler velocity measurement is extended to include the effect of the difference of the beam-pointing directions, and a simulation confirmed the formula. The results show that the uncertainty increases from 0.21 to 0.38 m/s for a case of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S/N = 20$ </tex-math></inline-formula> dB when one antenna footprint center is displaced from the other by half of the half-power beamwidth.

RCFusion: Fusing 4-D Radar and Camera With Bird’s-Eye View Features for 3-D Object Detection

Camera and millimeter-wave (MMW) radar fusion is essential for accurate and robust autonomous driving systems. With the advancement of radar technology, next-generation high-resolution automotive radar, i.e., 4D radar, has emerged. In addition to the target range, azimuth, and Doppler velocity measurements of traditional radar, 4D radar provides elevation measurement to create a denser “point cloud.” In this study, we propose a camera and 4D radar fusion network called RCFusion, which achieves multimodal feature fusion under a unified bird’s-eye view (BEV) space to accomplish 3D object detection tasks. In the camera stream, multi-scale feature maps are obtained by the image backbone and feature pyramid network; they are then converted into orthographic feature maps by an orthographic feature transform. Next, enhanced and fine-grained image BEV features are obtained via a designed shared attention encoder. Meanwhile, in the 4D radar stream, a newly designed component named Radar PillarNet efficiently encodes the radar features to generate radar pseudo-images, which are fed into the point cloud backbone to create radar BEV features. An interactive attention module is proposed for the fusion stage, which outputs a valid fusion of the two-modal BEV features. Finally, a generic detection head predicts the object classes and locations. The proposed RCFusion is validated on the TJ4DRadSet and View-of-Delft datasets. The experimental results and analysis show that the proposed method can effectively fuse camera and 4D radar features to achieve robust detection performance.

Impact of hydraulic forces on the passage of round goby (Neogobius melanostomus), gudgeon (Gobio gobio) and bullhead (Cottus gobio) in a vertical slot fish pass

AbstractEvery fish migrating upstream through vertical slot fish passes must swim through slots, where the resistance force of flowing water could affect the passage success. We measured the hydraulic force acting on the body of preserved benthic fish in a vertical slot at different water discharge rates (80 and 130 L/s) to compare the hydraulic burden individual fish species (round goby Neogobius melanostomus Pallas, 1814, gudgeon Gobio gobio L. and bullhead Cottus gobio L.) must overcome. The forces measured in three spatial axes were then compared to acoustic Doppler velocity measurements and the passage probability of 39–45 live fish per species. Passage probability reduction of 28.26% for round goby and 39.29% for bullhead was observed at the higher water discharge. Gudgeon showed increased numbers of passages and approaches when larger hydraulic forces were experienced at 130 L/s compared to the lower water discharge. Gudgeon experienced significantly lower hydraulic forces (mean 0.27 N ± 0.12 standard deviation) compared to round goby (mean 0.32 N ± 0.12 SD) and bullhead (0.35 N ± 0.14 SD). Potentially, the increased hydraulic forces at the higher water discharge contributed to the reduction in passages in round goby and bullhead. That gudgeon behaved differently from the other species illustrates how fish species deal differently with flowing water and the hydraulic forces experienced. Our approach provides a species‐oriented assessment of the flow field in ecologically relevant fish passes. These findings represent an important step towards the development of purposeful fish pass designs, which is essential for ecosystem‐oriented river connectivity.

Sub-surface plasma flows and the flare productivity of solar active regions

The extreme space weather conditions resulting from high energetic events likes solar flares and Coronal Mass Ejections demand for reliable space weather forecasting. The magnetic flux tubes while rising through the convection zone gets twisted by the turbulent plasma flows, energizing the system and resulting in flares. We investigate the relationship between the subsurface plasma flows associated with flaring active regions and their surface magnetic flux and current helicity. The near-surface horizontal velocities derived from the ring-diagram analysis of active region patches using Global Oscillation Network Group Doppler velocity measurements are used to compute the fluid dynamics descriptors like vertical divergence, vorticity and kinetic helicity used in this work. The flaring active regions are observed to have large value of vertical vorticity and kinetic helicity. Also, the horizontal flow divergence, vorticity, flux, kinetic and current helicities are observed to be significantly correlated and evolve in phase with each other. We observe that the integrated values of the above flow and magnetic parameters observed 1 day prior to the flare are significantly correlated with the integrated flare intensity of the active region. Hence, we show that strong vorticity/kinetic helicities lead to larger active region twisting, presumably generating high-intensity flares.

Improving Ego-Velocity Estimation of Low-Cost Doppler Radars for Vehicles

A low-cost automotive radar is often used in autonomous driving and advanced driver-assistance systems. However, the low cost radar often assumes all detected objects to be on the ground plane when estimating radar/vehicle ego-velocity, but when there are elevated background objects present, such as buildings and tall trees, the ego-velocity estimation tends to be biased. Here we analyze the source of estimation error and develop a new algorithm to recognize three types of object reflections using the discrepancy between the estimated ego-velocity and the measured Doppler velocity. We propose an elevation and background aware cost (EBAC) function to formulate an optimization framework which can distinguish the object types to improve ego-velocity estimation. We combine a robust estimation method with the optimization framework to handle outliers in radar readings. We have implemented the algorithm and tested it in both simulation and physical experiments using our autonomous vehicle. The results show that our estimation method significantly reduces ego-velocity estimation error while maintaining a smaller error variance without losing robustness. More specifically, it reduces the ego-velocity estimation error by 49% in the most common driving scenario.