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- W207629518 abstract "This thesis describes the application of the Cramer Rao inequality to synthetic aperture radar (SAR). The resulting Cramer Rao lower bounds (CRB) reveal the smallest possible error variances for target parameter estimates (position and reflectivity), regardless of the estimator or SAR processing. The performance bounds are consequently used to design synthetic arrays and evaluate multistatic SAR configurations. We show that optimal sensor arrangements of synthetic arrays offer the possibility for single-pass, monostatic SAR to mitigate an effect known as layover that often distorts radar imagery. We also show these nonlinear apertures can be used to image targets in three dimensions. Furthermore, a new detection/estimation algorithm for targets in SAR imagery is developed and mean-squared errors from Monte Carlo simulations are compared to corresponding CRBs. Finally, a dynamic model-based estimation algorithm is developed for SAR to localize targets behind building walls. This iterative, optimization technique shows the potential to avert the combinatorial complexity and local maximization associated with many classification problems requiring model-based solutions." @default.
- W207629518 created "2016-06-24" @default.
- W207629518 creator A5041600189 @default.
- W207629518 date "2021-05-10" @default.
- W207629518 modified "2023-09-23" @default.
- W207629518 title "Performance bounds, optimization and estimation techniques for synthetic aperture radar" @default.
- W207629518 doi "https://doi.org/10.17760/d10018700" @default.
- W207629518 hasPublicationYear "2021" @default.
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