Coordinated Ionospheric Reconstruction CubeSat Experiment (CIRCE) mission overview

The Coordinated Ionospheric Reconstruction Cubesat Experiment (CIRCE) is a joint US/UK mission consisting of two 6U CubeSats actively maintaining a lead-follow configuration in the same low Earth orbit with a launch planned for the 2020 timeframe. These nanosatellites will each feature multiple space weather payloads. From the US, the Naval Research Laboratory will provide two 1U Triple Tiny Ionospheric Photometers (Tri-TIPs) on each satellite, observing the ultraviolet 135.6 nm emission of atomic oxygen at nighttime. The primary objective is to characterize the twodimensional distribution of electrons in the Equatorial Ionization Anomaly (EIA). The methodology used to reconstruct the nighttime ionosphere employs continuous UV photometry from four distinct viewing angles in combination with an additional data source, such as in situ plasma density measurements, with advanced image space reconstruction algorithm tomography techniques. From the UK, the Defence Science and Technology Laboratory (Dstl) is providing the In-situ and Remote Ionospheric Sensing suite consisting of an Ion/Neutral Mass Spectrometer, a triple-frequency GPS receiver for ionospheric sensing, and a radiation environment monitor. We present our mission concept, simulations illustrating the imaging capability of the Tri-TIP sensor suite, and a range of science questions addressable via these measurements

Alfvén waves underlying ionospheric destabilization: ground-based observations

During geomagnetic storms, terawatts of power in the million mile-per-hour solar wind pierce the Earth’s magnetosphere. Geomagnetic storms and substorms create transverse magnetic waves known as Alfvén waves. In the auroral acceleration region, Alfvén waves accelerate electrons up to one-tenth the speed of light via wave-particle interactions. These inertial Alfvén wave (IAW) accelerated electrons are imbued with sub-100 meter structure perpendicular to geomagnetic field B. The IAW electric field parallel to B accelerates electrons up to about 10 keV along B. The IAW dispersion relation quantifies the precipitating electron striation observed with high-speed cameras as spatiotemporally dynamic fine structured aurora. A network of tightly synchronized tomographic auroral observatories using model based iterative reconstruction (MBIR) techniques were developed in this dissertation. The TRANSCAR electron penetration model creates a basis set of monoenergetic electron beam eigenprofiles of auroral volume emission rate for the given location and ionospheric conditions. Each eigenprofile consists of nearly 200 broadband line spectra modulated by atmospheric attenuation, bandstop filter and imager quantum efficiency. The L-BFGS-B minimization routine combined with sub-pixel registered electron multiplying CCD video stream at order 10 ms cadence yields estimates of electron differential number flux at the top of the ionosphere. Our automatic data curation algorithm reduces one terabyte/camera/day into accurate MBIR-processed estimates of IAW-driven electron precipitation microstructure. This computer vision structured auroral discrimination algorithm was developed using a multiscale dual-camera system observing a 175 km and 14 km swath of sky simultaneously. This collective behavior algorithm exploits the “swarm” behavior of aurora, detectable even as video SNR approaches zero. A modified version of the algorithm is applied to topside ionospheric radar at Mars and broadcast FM passive radar. The fusion of data from coherent radar backscatter and optical data at order 10 ms cadence confirms and further quantifies the relation of strong Langmuir turbulence and streaming plasma upflows in the ionosphere with the finest spatiotemporal auroral dynamics associated with IAW acceleration. The software programs developed in this dissertation solve the century-old problem of automatically discriminating finely structured aurora from other forms and pushes the observational wave-particle science frontiers forward

Elevation and Deformation Extraction from TomoSAR

3D SAR tomography (TomoSAR) and 4D SAR differential tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to provide an essential innovation of SAR Interferometry for many applications, sensing complex scenes with multiple scatterers mapped into the same SAR pixel cell. However, these are still influenced by DEM uncertainty, temporal decorrelation, orbital, tropospheric and ionospheric phase distortion and height blurring. In this thesis, these techniques are explored. As part of this exploration, the systematic procedures for DEM generation, DEM quality assessment, DEM quality improvement and DEM applications are first studied. Besides, this thesis focuses on the whole cycle of systematic methods for 3D & 4D TomoSAR imaging for height and deformation retrieval, from the problem formation phase, through the development of methods to testing on real SAR data. After DEM generation introduction from spaceborne bistatic InSAR (TanDEM-X) and airborne photogrammetry (Bluesky), a new DEM co-registration method with line feature validation (river network line, ridgeline, valley line, crater boundary feature and so on) is developed and demonstrated to assist the study of a wide area DEM data quality. This DEM co-registration method aligns two DEMs irrespective of the linear distortion model, which improves the quality of DEM vertical comparison accuracy significantly and is suitable and helpful for DEM quality assessment. A systematic TomoSAR algorithm and method have been established, tested, analysed and demonstrated for various applications (urban buildings, bridges, dams) to achieve better 3D & 4D tomographic SAR imaging results. These include applying Cosmo-Skymed X band single-polarisation data over the Zipingpu dam, Dujiangyan, Sichuan, China, to map topography; and using ALOS L band data in the San Francisco Bay region to map urban building and bridge. A new ionospheric correction method based on the tile method employing IGS TEC data, a split-spectrum and an ionospheric model via least squares are developed to correct ionospheric distortion to improve the accuracy of 3D & 4D tomographic SAR imaging. Meanwhile, a pixel by pixel orbit baseline estimation method is developed to address the research gaps of baseline estimation for 3D & 4D spaceborne SAR tomography imaging. Moreover, a SAR tomography imaging algorithm and a differential tomography four-dimensional SAR imaging algorithm based on compressive sensing, SAR interferometry phase (InSAR) calibration reference to DEM with DEM error correction, a new phase error calibration and compensation algorithm, based on PS, SVD, PGA, weighted least squares and minimum entropy, are developed to obtain accurate 3D & 4D tomographic SAR imaging results. The new baseline estimation method and consequent TomoSAR processing results showed that an accurate baseline estimation is essential to build up the TomoSAR model. After baseline estimation, phase calibration experiments (via FFT and Capon method) indicate that a phase calibration step is indispensable for TomoSAR imaging, which eventually influences the inversion results. A super-resolution reconstruction CS based study demonstrates X band data with the CS method does not fit for forest reconstruction but works for reconstruction of large civil engineering structures such as dams and urban buildings. Meanwhile, the L band data with FFT, Capon and the CS method are shown to work for the reconstruction of large manmade structures (such as bridges) and urban buildings

Optimizing MIDAS III over South Africa

In this thesis an ionospheric tomographic algorithm called Multi-Instrument Data Anal- ysis System (MIDAS) is used to reconstruct electron density profiles using the Global Positioning System (GPS) data recorded from 53 GPS receivers over the South African region. MIDAS, developed by the Invert group at the University of Bath in the UK, is an inversion algorithm that produces a time dependent 3D image of the electron density of the ionosphere. GPS receivers record the time delay and phase advance of the trans- ionospheric GPS signals that traverse through the ionosphere from which the ionospheric parameter called Total Electron Content (TEC) can be computed. TEC, the line integral of the electron density along the satellite-receiver signal path, is ingested by ionospheric tomographic algorithms such as MIDAS to produce a time dependent 3D electron density profile. In order to validate electron density profiles from MIDAS, MIDAS derived NmF2 values were compared with ionosonde derived NmF2 values extracted from their respective 1D electron density profiles at 15 minute intervals for all four South African ionosonde stations (Grahamstown, Hermanus, Louisvale, and Madimbo). MIDAS 2D images of the electron density showed good diurnal and seasonal patterns; where a comparison of the 2D images at 12h00 UT for all the validation days exhibited maximum electron concentration during the autumn and summer and a minimum during the winter. A root mean square error (rmse) value as small as 0.88x 10¹¹[el=m³] was calculated for the Louisvale ionosonde station during the winter season and a maximum rmse value of 1.92x 10¹¹[el=m³] was ob- tained during the autumn season. The r² values were the least during the autumn and relatively large during summer and winter; similarly the rmse values were found to be a maximum during the autumn and a minimum during the winter indicating that MIDAS performs better during the winter than during the autumn and spring seasons. It is also observed that MIDAS performs better at Louisvale and Madimbo than at Grahamstown and Hermanus. In conclusion, the MIDAS reconstruction has showed good agreement with the ionosonde measurements; therefore, MIDAS can be considered a useful tool to study the ionosphere over the South African region

Dynamics of Particle Precipitation in the Auroral Ionosphere.

Energetic electrons deposit significant amounts of energy into the ionosphere during precipitation events. Riometers provide a means of monitoring this precipitation by measuring the associated cosmic noise absorption. Individually however they cannot provide any details about the energies of the precipitating electrons. The first study in this thesis looks at estimating the characteristic energy of the precipitating electrons by the means of two imaging riometers with overlapping fields of view. Two methods of calculating the height of maximum cosmic noise absorption are developed, a method of height triangulation and tomographic reconstruction of the absorption events. These methods show a marked improvement when compared with a previously published method. A case study comparing the calculated height of maximum cosmic noise absorption with a deduced absorption profile from an EISCAT electron density profile shows good correlation. Using the height of maximum cosmic noise absorption estimates are made of the characteristic energy for three case studies; a morning absorption event, a substorm spike and an afternoon absorption event. The estimated energies for these events were 5keV, 17-20keV and 100+keV respectively. The second study concerns the statistics and mechanisms of daytime absorption events. Statistics of absorption during the course of a day show a deep minimum during the afternoon sector. However there are a number of discrete cases that do occur during this afternoon minimum. A statistical analysis of the time period, 12-16UT at Kilpisjarvi is undertook. They are found to be short lived, highly localised events. This is in agreement with previous studies. They tend to occur during periods of weak geomagnetic activity. A portion of these are found to be early onset substorms, and account for 7.4% of the events. To understand the mechanisms behind the rest of the events one year of data was analyised in greater detail. A portion of these events seem to agree with previous studies, that these events are reltavistic preciptiation events caused by ElectroMagnetic Ion Cyclotron (EMIC) wave scattering. However a greater number of the events seem to be due to the precipitation of lower energy electrons during dispersed electron injections of the radiation belts; a more localized and later occurring version of morning absorption caused by the eastward drift and scattering of lower energy (10-100keV) substorm injected electrons

A sparsity-driven approach for joint SAR imaging and phase error correction

Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements it provides over existing techniques for model error compensation in SAR