Spatia1 sound acquisition in Higher-Order Ambisonics (HOA) is constrained by hardware complexity and storage space. In contrast, the low order ambisonics (B-format Signals) suffers from low spatial resolution. So it is worthwhile to acquire the sound at low order to reduce hardware complexity and storage requirement and upscale to a higher order while reproducing to improve the spatial resolution. In this work, a sparse framework is formulated that efficiently uses the Order Recursive Matching Pursuit (ORMP) algorithm for Multiple Measurement Vectors (MMV) to decompose the low-order encoded signal. Subsequently, the upscaled HOA signal is obtained from the decomposed low-order ambisonics to reproduce the spatial audio with high spatial resolution. The performance of the proposed upscaling method is evaluated using the metrics such as a Mean Square Error (MSE) in upscaled signals and error in the reproduced sound field. The subjective evaluation is carried out using a listening test and compared with state-of-art methods.