Frank Kirchner bei Wissen um 11 Professor Dr. Sincethe university professor holds head of the chair for robotics in the special field mathematics and informatics.
This practice has historically been driven by available hardware storage, processing capabilities, and data link capacity.
Recent advances in high performance computing HPC have enabled extremely dense storage and processing solutions. Therefore, previous motives for discarding radar phase information in ATR applications have been mitigated.
First, we characterize the value of phase in one-dimensional 1-D radar range profiles and two dimensional 2-D SAR imagery with respect to the ability to correctly estimate target features, which are currently employed in ATR algorithms for target discrimination.
These features correspond to physical characteristics of a target through radio frequency RF scattering phenomenology. Physics-based electromagnetic scattering models developed from the geometrical theory of diffraction are utilized for the information analysis presented here.
Information is quantified by the error of target parameter estimates from noisy radar signals when phase is either retained or discarded.
Operating conditions OCs of signal-to-noise ratio, bandwidth, and aperture extent are considered. Second, we investigate the value of phase in 1-D radar returns with respect to the ability to correctly classify canonical targets.
These classification techniques maintain varying assumptions on the observed data set, with the BER bound making no assumptions.
In Synthetic aperture radar thesis case, phase information is demonstrated to improve radar target classification rates. Based on previous results from in situ observations of monitoring sites, climate-driven models, and SBAS-InSAR observations, the averaged active layer thickness along the Qinghai-Tibet railway QTR ranges from less than 1 m to more than 5 m.
The rate of increase of the ALT is approximately 6. We found that the ALT is largely thickening, and with distinct spatial variations, for the first time, over a large permafrost-covered region of the northern Qinghai-Tibetan Plateau.
The result of this study has implications of improving our understanding in the alpine surface and subsurface cryospheric-hydrologic processes, ecosystem change, runoff changes in headwaters for some of the largest rivers in Asia, and the stability of human infrastructures over the Qinghai-Tibetan Plateau.
We further demonstrated that the wetland water level changes in the Sundarbans mangrove forest wetland, Bangladesh could be effectively observed by integrating L-band SAR intensity imagery and radar altimetry data.
This technique is based on an assumption that the dominant backscattering mechanism in mangrove forests is double-bounce backscattering, at the same time, the double-bounce backscattering is weakened because of water level rising.
In order to demonstrate this assumption, we computed interferogram coherence, a byproduct of InSAR processing, and averaged backscattering coefficient during wet season and dry season over our study regions. We found that the L-band SAR backscatter coefficient in wetlands is inversely proportional with water level in the mangrove forest.
Finally, the SAR backscattering coefficient is then used to estimate high-resolution 30 m water level time series, covering the study region.
SAR-inferred water level time series show significant spatial and temporal variability Committee: Target classification using SAR imagery is a challenging problem due to large variations of target signature as the target aspect angle changes.
Previous work on modeling wide angle SAR imagery has shown that point features, extracted from scattering center locations, result in a high dimensional feature vector that lies on a low dimensional manifold.
We propose to use rich probabilistic models for these target manifolds to analyze classification performance as a function of Signal-to-noise ratio SNR and Bandwidth.
We employ Mixture of Factor Analyzers MoFA models to approximate the target manifold locally, and use error bounds for the estimation and analysis of classification error performance. We compare our performance predictions with the empirical performance of practical classifiers using simulated wideband SAR signatures of civilian vehicles.
We then extend this work to design optimal maximally discriminative projections MDP for the manifold structured data. An optimization algorithm is proposed that maximizes the Kullback Leibler KL -divergence between two mixture models through optimizing the closed-form "Variational Approximation" of the KL-divergence between the MoFA models.
We then propose to generalize our MDP dimensionality reduction technique to multi-class using non-linear constrained optimization through minimax quasi-Newton methods.High-resolution radar imaging is an area undergoing rapid technological and scientiﬁc development.
Synthetic Aperture Radar (SAR) and Inverse Synthetic Aperture Radar (ISAR) are imaging radars with an ever-increasing number of applications for both civilian and military users.
A description of how Cameron decomposition is implemented in Polarimetric Synthetic Aperture Radar (PolSAR) signature detection is also presented. Cameron et al, have developed a method of decomposing scatterer scattering matrices based on Huynen’s .
Matthew Schlutz - Synthetic Aperture Radar Imaging Simulated in MATLAB | iv Abstract Synthetic Aperture Radar Imaging Simulated in MATLAB Matthew Schlutz This thesis further develops a method from ongoing thesis projects with the goal of generating images using synthetic aperture radar (SAR) simulations coded in MATLAB.
Synthetic aperture radar (SAR) is an imaging technique based on the radio reflectivity of the target being imaged. SAR instruments offer many advantages over optical imaging due to the ability to form coherent images in inclement weather, at night, and through ground cover.
This thesis sets out the results of work done in preparation for a South African Airborne Synthetic Aperture Radar (SAR) measuring campaign envisaged for /5. Automatic Target Recognition of Synthetic Aperture Radar Images using Elliptical Fourier Descriptors by Automatic Target Recognition of Synthetic Aperture Radar Images Using Elliptical the viewer to clearly see the shape of the target vehicles in the radar image.
This thesis only involves the ten targets shown in Table These.