The Research Of Satellite Navigation Signal Acquisition Based On Compressive Sensing

The satellite navigation system plays an important role in economic and social development and military strategy, and more and more countries and regions are paying their attention to the research and development of satellite navigation systems. As the first step of signal processing in the receiver, the signal acquisition, which usually costs a large number of hardware and computation resources, is essential for the subsequent signal processing operations. The signal acquisition algorithms, especially for fast acquisitions, have attracted extensive attentions of researchers world wide. In this thesis, the compressive sensing(CS) theory is applied to develop signal acquisition algorithm for satellite navigation, in order to reduce the computational complexity and hardware costs of the receiver.This thesis is structured with a goal of developing compressive sensing based satellite navigation signal acquisition algorithms. The study begins with the traditional signal acquisition algorithms and the compressive sensing theory. For the traditional satellite navigation signal acquisition algorithms, we propose an improved short code acquisition algorithm – peak-constant zero-padding(PCZP), which has much more stable detection performance than the zero-padding(ZP) method. In the theory of compressive sensing, we propose an improved orthogonal matching pursuit(OMP) recovery algorithm – block-refined OMP(BROMP). Compared to the OMP algorithm, the proposed algorithm can achieve better performance, still with less complexity. In the end, the CS based satellite navigation signal model is explored, and we propose two acquisition schemes based on compressive sensing, one for short code, and the other for long code, to reduce the matching times of the acquisition process, and thus reduce the complexity. In the CS based scheme, the measurement matrix satisfies the lower bound condition of the Welch bound theory, in order to minimize the SNR loss. The CS based signal acquisition algorithm provides a trade-off between performance and complexity.