Abstract: Signal acquisition is one of the key tasks in global position system (GPS) baseband signal processing, which determines the power consumption and the hardware cost of GPS receivers. However, most conventional acquisition algorithms are based on the correlation operations, thus demanding a large amount of data and consuming lots of computational resources. To alleviate this, we propose an efficient parallel GPS signal acquisition algorithm in this paper, utilizing the idea of compressive sensing. Specifically, we first represent the GPS signals in sparse form by projecting the signal onto a base matrix consisting of the orthogonal C/A codes. Based on this sparse representation, a compressive sensing model of GPS signal acquisition is established. Then, we develop an efficient iterative parallel acquisition algorithm for the compressive sensing problem by fitting it into the framework of alternating direction method of multipliers (ADMM). Each iteration of ADMM can be computed in closed form, thus giving it very low complexity. The efficiency and efficacy of the proposed algorithm are validated by numerical simulations.