Collective detection is a promising approach to positioning in a weak signal environment, in which the navigation solution is directly obtained by acquisition search in a multi-dimensional position and common clock bias uncertainty space. By combining the correlation values from multiple satellites and fully utilizing the coherence between them, the detectable C/N0 of individual satellites can be lowered. However, the lack of a computationally efficient optimization algorithm due to high dimensionality and complexity has hindered its application. A multi-resolution collective detection is therefore proposed to be a coarse-to-fine searching approach to solve for the position and common clock bias estimation. Although it reduces the computation time of collective detection, there is a gap in the efficiency study, which is the contribution of this research. The features of different levels of search in a multi-resolution algorithm are investigated. For a coarse search with large horizontal position step size, a smaller common clock bias step size is proposed instead of an averaging correlogram to reduce computation complexity as well as to obtain high time resolution. For the fine search with small horizontal space step size, a 3-D Dichotomous searching scheme is designed and applied to reduce the number of searching grids. Computer simulation results using experimental raw data are provided, to demonstrate the performance improvement against the conventional methods.