Inverse Synthetic Aperture LADAR Image Construction: An inverse model-based approach

Standard Synthetic Aperture LADAR (SAL) image processing techniques use fast Fourier transforms (FFTs) to render images. This leads to noise amplification and estimates of the complex-valued reflection coefficient, thus resulting in high variation known as speckle. In this paper we propose a model-based iterative reconstruction (MBIR) approach using a Bayesian framework to form SAL images. The resulting images are the maximum a posteriori (MAP) estimate of object's real-valued surface reflectance. To overcome the complexity of the MAP cost function, we use the expectation maximization (EM) algorithm to derive a surrogate function which is then optimized. The proposed algorithm is tested on simulated data and compared against the Fourier-based approach.