A cost effective method to improve the space surveillance mission performance of United States Air Force (USAF) ground-based telescopes is investigated and improved. A minimum variance wavefront estimation technique is used to improve Deconvolution from Wavefront Sensing (DWFS), a method to mitigate the effects of atmospheric turbulence on imaging systems that does not require expensive adaptive optics. Both least-squares and minimum variance wavefront phase estimation techniques are investigated, using both Gaussian and Zernike polynomial elementary functions. Imaging simulations and established performance metrics are used to evaluate these wavefront estimation techniques for a one-meter optical telescope. Performance metrics include the average pupil-averaged mean square phase error of the residual wavefront, the average system transfer function, the signal-to-noise ratio (SNR) of the system transfer function, and the optical transfer function correlation. Results show that the minimum variance estimation technique that employs Zernike polynomial elementary functions offers improvements over all other estimation techniques in each of the performance metrics. Extended object simulations are also conducted which demonstrate the improvements in image quality and resolution that result from the modifications to the DWFS method. Implementation of the DWFS method into USAF space surveillance telescopes is investigated
