| Summary: | 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.
|
|---|
