Scattering Characteristic Extraction Method for Manmade Target Based on Target Null Theory

Scattering characteristic extraction is an essential part of manmade target recognition. However, if two scattering points are in adjacent pixels, scattering characteristic extraction may fail to acquire accurate polarimetric scattering matrices (PSMs) of the weak scattering points due to the contamination caused by the strong scattering points. Target null theory provides a way to solve this problem. By selecting the transmitting and receiving polarization states of radar antennas simultaneously, the echo power of a strong scattering point becomes zero and the contamination effect is avoided. In this paper, a method based on target null theory for scattering characteristic extraction is proposed. First, we optimize the transmitting and receiving polarization states of the radar antenna to suppress the intensities of the strong scattering points to highlight the positions of the weak scattering points in certain polarimetric channels. Second, to suppress the contamination effects of strong scattering points in other polarimetric channels, we establish perturbation correction equations to erase the error generated by the point spread function (PSF) among adjacent scattering points in the radar image. Finally, the solved polarimetric scattering matrices of corresponding positions are implemented for target retrieval. The electromagnetic simulation results demonstrate the effectiveness of the proposed method

Real aperture synthetically organised radar

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Signal processing for ground penetrating radar

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Snow Depth Retrieval from Wide Band Radar in Trail Valley Creek

Snow is an essential component in hydrology systems, and more speci cally, monitoring variations in snow cover can provide valuable information about water supply, wildlife habitats, and climate changes. In recent years, the potential of wide band snow radar (e.g. 2-8 GHz) has been discovered with a series of campaigns in Operation IceBrdige (OIB). Due to data from OIB mainly focusing on sea ice, most of the algorithms were also developed for snow on sea ice. As a result, this thesis aimed to test the applicability of the interface-based pulse peakiness snow depth retrieval method to snow on land. In addition, due to the common usage of radar altimeter in sea ice classi cation, this thesis also explored the possibility of adapting some of the ideas in sea ice classi cation to develop another retrieval method. Both approaches were tested on the 6 major vegetation types (tree, tall shrub, riparian shrub, dwarf shrub, tussock, and lichen) in the study area. Snow depth derived from Airborne Laser Scanner (ALS) point clouds was used as the reference for snow depth retrieval. Running the recalibrated pulse peakiness algorithm yielded a Mean Absolute Error (MAE) of 12 cm, 27 cm, 29 cm, 13 cm, 9 cm, and 10 cm for tree, tall shrub, riparian shrub, dwarf shrub, tussock and lichen respectively. It was concluded that the principles behind the pulse peakiness approach is valid for snow on land. The presence of surface vegetation and the hummocky terrain of the study area did present some considerable challenges. In comparison, the classi cation approach using K-means while produced some accurate results under speci c situations, was not as robust as the existing pulse peakiness approach. Although, it was argued that the classi cation approach was in early stages and there were some potential for better results

Shuttle imaging radar-C science plan

The Shuttle Imaging Radar-C (SIR-C) mission will yield new and advanced scientific studies of the Earth. SIR-C will be the first instrument to simultaneously acquire images at L-band and C-band with HH, VV, HV, or VH polarizations, as well as images of the phase difference between HH and VV polarizations. These data will be digitally encoded and recorded using onboard high-density digital tape recorders and will later be digitally processed into images using the JPL Advanced Digital SAR Processor. SIR-C geologic studies include cold-region geomorphology, fluvial geomorphology, rock weathering and erosional processes, tectonics and geologic boundaries, geobotany, and radar stereogrammetry. Hydrology investigations cover arid, humid, wetland, snow-covered, and high-latitude regions. Additionally, SIR-C will provide the data to identify and map vegetation types, interpret landscape patterns and processes, assess the biophysical properties of plant canopies, and determine the degree of radar penetration of plant canopies. In oceanography, SIR-C will provide the information necessary to: forecast ocean directional wave spectra; better understand internal wave-current interactions; study the relationship of ocean-bottom features to surface expressions and the correlation of wind signatures to radar backscatter; and detect current-system boundaries, oceanic fronts, and mesoscale eddies. And, as the first spaceborne SAR with multi-frequency, multipolarization imaging capabilities, whole new areas of glaciology will be opened for study when SIR-C is flown in a polar orbit

FMCW Radar signal processing for Antarctic Ice Shelf profiling and imaging

This thesis contains details of all the signal processing work being done on FMCW Radar (operating at VHF-UHF band) for the Antarctic Ice Shelf monitoring project that has been carried out at UCL. The system developed at UCL was based on a novel concept of phase-sensitive FMCW radar with low power consumption, thus allowing data collection for long period of time with millimetre range precision. Development of new signal processing method was required in order to process the large amount of data, along with the signal processing technique for obtaining the high precision range values. This was achieved during the first stage of the thesis, providing accurate ice shelf basal layer melt rate values. Properties of the FMCW radar system and experimental scenarios posed further signal processing challenges. Those challenges were met by developing number of novel algorithms. A novel shape matching algorithm was developed to detect internal layers underneath the ice shelf. Range migration correction method was developed to compensate for the defocusing of the image in large angles due to high fractional bandwidth of the radar system. Vertical error correction method was developed to compensate for any vertical displacement of the radar antenna during field experiment. Finally, a novel 3-D MIMO imaging algorithm for the Antarctic ice shelf base study was developed. This was done to process the 8x8 MIMO radar (developed at UCL) data. The radars have been deployed in the Antarctica during the Austral summer of each year from 2011-2014. The field experiments were done in the Ronne, Larsen-C, Larsen North, George VI and Ross ice shelves. The novel signal processing techniques have been successfully applied on the real data, allowing better understanding of the Antarctic ice shelf features

Development of high-precision snow mapping tools for Arctic environments

Le manteau neigeux varie grandement dans le temps et l’espace, il faut donc de nombreux points d’observation pour le décrire précisément et ponctuellement, ce qui permet de valider et d’améliorer la modélisation de la neige et les applications en télédétection. L’analyse traditionnelle par des coupes de neige dévoile des détails pointus sur l’état de la neige à un endroit et un moment précis, mais est une méthode chronophage à laquelle la distribution dans le temps et l’espace font défaut. À l’opposé sur la fourchette de la précision, on retrouve les solutions orbitales qui couvrent la surface de la Terre à intervalles réguliers, mais à plus faible résolution. Dans l’optique de recueillir efficacement des données spatiales sur la neige durant les campagnes de terrain, nous avons développé sur mesure un système d’aéronef télépiloté (RPAS) qui fournit des cartes d’épaisseur de neige pour quelques centaines de mètres carrés, selon la méthode Structure from motion (SfM). Notre RPAS peut voler dans des températures extrêmement froides, au contraire des autres systèmes sur le marché. Il atteint une résolution horizontale de 6 cm et un écart-type d’épaisseur de neige de 39 % sans végétation (48,5 % avec végétation). Comme la méthode SfM ne permet pas de distinguer les différentes couches de neige, j’ai développé un algorithme pour un radar à onde continue à modulation de fréquence (FM-CW) qui permet de distinguer les deux couches principales de neige que l’on retrouve régulièrement en Arctique : le givre de profondeur et la plaque à vent. Les distinguer est crucial puisque les caractéristiques différentes des couches de neige font varier la quantité d’eau disponible pour l’écosystème lors de la fonte. Selon les conditions sur place, le radar arrive à estimer l’épaisseur de neige selon un écart-type entre 13 et 39 %. vii Finalement, j’ai équipé le radar d’un système de géolocalisation à haute précision. Ainsi équipé, le radar a une marge d’erreur de géolocalisation d’en moyenne <5 cm. À partir de la mesure radar, on peut déduire la distance entre le haut et le bas du manteau neigeux. En plus de l’épaisseur de neige, on obtient également des points de données qui permettent d’interpoler un modèle d’élévation de la surface solide sous-jacente. J’ai utilisé la méthode de structure triangulaire (TIN) pour toutes les interpolations. Le système offre beaucoup de flexibilité puisqu’il peut être installé sur un RPAS ou une motoneige. Ces outils épaulent la modélisation du couvert neigeux en fournissant des données sur un secteur, plutôt que sur un seul point. Les données peuvent servir à entraîner et à valider les modèles. Ainsi améliorés, ils peuvent, par exemple, permettre de prédire la taille, le niveau de santé et les déplacements de populations d’ongulés, dont la survie dépend de la qualité de la neige. (Langlois et coll., 2017.) Au même titre que la validation de modèles de neige, les outils présentés permettent de comparer et de valider d’autres données de télédétection (par ex. satellites) et d’élargir notre champ de compréhension. Finalement, les cartes ainsi créées peuvent aider les écologistes à évaluer l’état d’un écosystème en leur donnant accès à une plus grande quantité d’information sur le manteau neigeux qu’avec les coupes de neige traditionnelles.Abstract: Snow is highly variable in time and space and thus many observation points are needed to describe the present state of the snowpack accurately. This description of the state of the snowpack is necessary to validate and improve snow modeling efforts and remote sensing applications. The traditional snowpit analysis delivers a highly detailed picture of the present state of the snow in a particular location but lacks the distribution in space and time as it is a time-consuming method. On the opposite end of the spatial scale are orbital solutions covering the surface of the Earth in regular intervals, but at the cost of a much lower resolution. To improve the ability to collect spatial snow data efficiently during a field campaign, we developed a custom-made, remotely piloted aircraft system (RPAS) to deliver snow depth maps over a few hundred square meters by using Structure-from-Motion (SfM). The RPAS is capable of flying in extremely low temperatures where no commercial solutions are available. The system achieves a horizontal resolution of 6 cm with snow depth RMSE of 39% without vegetation (48.5% with vegetation) As the SfM method does not distinguish between different snow layers, I developed an algorithm for a frequency modulated continuous wave (FMCW) radar that distinguishes between the two main snow layers that are found regularly in the Arctic: “Depth Hoar” and “Wind Slab”. The distinction is important as these characteristics allow to determine the amount of water stored in the snow that will be available for the ecosystem during the melt season. Depending on site conditions, the radar estimates the snow depth with an RMSE between 13% and 39%. v Finally, I equipped the radar with a high precision geolocation system. With this setup, the geolocation uncertainty of the radar on average < 5 cm. From the radar measurement, the distance to the top and the bottom of the snowpack can be extracted. In addition to snow depth, it also delivers data points to interpolate an elevation model of the underlying solid surface. I used the Triangular Irregular Network (TIN) method for any interpolation. The system can be mounted on RPAS and snowmobiles and thus delivers a lot of flexibility. These tools will assist snow modeling as they provide data from an area instead of a single point. The data can be used to force or validate the models. Improved models will help to predict the size, health, and movements of ungulate populations, as their survival depends on it (Langlois et al., 2017). Similar to the validation of snow models, the presented tools allow a comparison and validation of other remote sensing data (e.g. satellite) and improve the understanding limitations. Finally, the resulting maps can be used by ecologist to better asses the state of the ecosystem as they have a more complete picture of the snow cover on a larger scale that it could be achieved with traditional snowpits