RESUMO
In order to improve speed and efficiency over traditional scanning methods, a Bayesian compressive sensing algorithm using adaptive spatial sampling is developed for single detector millimeter wave synthetic aperture imaging. The application of this algorithm is compared to random sampling to demonstrate that the adaptive algorithm converges faster for simple targets and generates more reliable reconstructions for complex targets.
RESUMO
The combination of wide bandwidth W-band inverse synthetic aperture radar imagery and high-fidelity numerical simulations has been used to identify distinguishing signatures from simple metallic and dielectric targets. Targets are located with millimeter-scale accuracy using super-resolution techniques. Radon transform reconstructions of the returns from rotated targets approached the image quality of the complete data set in a fraction of the time by sampling as few as 10 angles. The limitations of shooting-and-bouncing ray simulations at high frequencies are illustrated through a critical comparison of their predictions with the measured data and the method of moments simulations, indicating the importance of accurately capturing the obfuscating role played by multipath interference in complex targets.