Fusion based Compressive Sensing (CS) reconstruction algorithms combine multiple CS reconstruction algorithms, which worked with different principles, to obtain a better signal estimate. Examples include Fusion of Algorithms for Compressed Sensing (FACS) and Committee Machine Approach for Compressed Sensing (CoMACS). However, these algorithms involve solving a least squares problem which may be ill-conditioned. Modified CS algorithms such as Modified Basis Pursuit (Mod-BP) ensured a sparse signal can efficiently be reconstructed when a part of its support is known. Since Mod-BP makes use of available signal knowledge to improve upon BP, we propose to employ multiple Greedy Pursuits (GPs) to derive a partial support for Mod-BP. As Mod-BP makes use of signal knowledge derived using GPs, we term our proposed algorithm as Greedy Pursuits Assisted Basis Pursuit (GPABP). Experimental results show that our proposed algorithm performs better than the state-of-the-art algorithms - FACS and its variants. © 2015 EURASIP.