# A Fine Fast Acquisition Scheme for a Communication and Navigation Fusion System
## 1 Introduction
Parallel search can be realized using fast Fourier transform (FFT) or massive parallel correlators but the drawback of huge resource consumption cannot be avoided [22; 23; 24]. The time spent in testing a cell is usually identified as dwell time, and multiple dwell techniques are utilized to reduce MAT. Typical double dwell (DD) structure divides the acquisition into two stages: coarse acquisition stage and fine acquisition stage. Different search steps, integration method (coherent, non-coherent) or integration time are utilized in different dwell stage [14; 16; 25; 26; 27; 28]. Folding and dual folding techniques are proposed in [15] to reduce the code phase search space by folding the receiver replica PRN code and the incoming signal, and the correlation is performed using FFT. However, the improvement of MAT (decreases) is at the expense of lower signal to noise ratio (SNR). Reference [25] uses compressed sensing (CS) technique in acquisition, which can exploit the sparsity of received GNSS signal and reduce the number of required correlators. Two-dimensional compressed correlator (TDCC) is introduced in [26; 27] to compress the code phase and frequency search space by coherently combining the signal energy in the neighboring hypotheses. However, the SNR loss is also inevitable as the compression rate increases. The detection strategies are chosen so as to optimize some performance metrics such as MAT and PFA [13; 14; 17; 29; 30]. Constant false alarm rate (CFAR) algorithm and its effect on reducing the MAT are analyzed in literature [13]. Reference [29] adds threshold generation mode (TM) in DD serial search to obtain optimal threshold. However, the threshold crossing (TC) detection strategy used in both studies have worse detection performance than MAX criterion. Reference [14] combines the MAX criterion and threshold crossing (TC) criterion to realize a trade-off between detection performance, hardware complexity and acquisition time, which is called MAX/TC criterion. Reference [17] uses Bayesian approach to realize fine acquisition strategy but the prior probability of each cell in search space is needed. MAX/TC criteria is applied in DD structure and the influence of different integration is analyzed in literature [30]. However, the influence of a different threshold is not analyzed. The search space size reduction techniques mentioned above has its limitation if they are directly applied in CNFS. Compared with GNSS, the signal transmitter used in CNFS has a definite position which can benefit search space size reduction. On the other hand, the choice of detection strategies should consider the fluctuation of background interference, which causes the fluctuation of PFA and increases MAT significantly. In this paper, a novel fast acquisition method based on search space reduction (SSR) and DD-MAX/TC-CACFAR is proposed to realize fast acquisition with fine performance. Firstly, in the CAF evaluation stage, the SSR method reduces search space size and number based on adjacent transmitter information (ATI) and code phase prior information (CPPI). The ATI is obtained from the new designed navigation message or the server. The CPPI is obtained from the acquisition result of the PRN code which has been acquired. The coarse-to-fine double dwell structure is utilized to obtain accuracy frequency estimation without causing the increase of search space size. Secondly, in the signal detection stage, a novel detection strategy is used to realize fine detection performance. The MAX/TC criterion together with cell-averaging CFAR (CACFAR) is performed in double dwell structure, which is called DD-MAX/TC-CACFAR method. Therefore, the CNFS positioning receiver can have fine and stable detection performance when there is fluctuation of noise, which can also effectively reduce MAT. Comparative simulations and experiments indicate the superiority of the proposed method. Different performance corresponding to different parameters of the proposed detection strategy is firstly analyzed and comparisons with exists strategies are given.