Ambisonics is an efficient spatial sound acquisition and reproduction technique in the spherical harmonic domain. At low frequencies, lower-order ambisonics reproduction is accurate, but at high frequencies, the spatial resolution suffers. An increase in frequency shrinks the radius of the error-free region and degrades the spatial resolution. Higher-order ambisonics (HOA) provided better spatial resolution in this context. However, sound spatial acquisition in HOA is constrained by hardware complexity and storage space, in contrast to low-order ambisonics (B-format). So, it is worthwhile to acquire the sound scene at low order to reduce hardware complexity and storage requirement and upscale to a higher order while reproducing to improve the spatial resolution. This work investigated algorithms based on minimizing the diversity measures for obtaining higher-order ambisonics from the B-format signals. In particular, we are interested in the FOCUSS (FOCal Underdetermined System Solver) class of algorithms, which is an alternative and complementary approach to the sequential forward method. Also, a more robust regularized FOCUSS algorithm for the sparse inverse problem is investigated further. The performance of the proposed upscaling method is evaluated using the mean square error metrics. The subjective evaluation is performed using a listening test and compared with state-of-art methods.