Rearch Of Improved Compass Pseudo-range Positioning Algorithm Based On PSO

Compass navigation positioning system is a self-developed navigation system of China,With the system is more and more perfect, the coverage is more and more wide, the development and application of the Compass navigation system is also more and more broad.In recent years, the positioning of the Compass navigation system is a hot topic, especially,the methods of improving the accuracy of the Compass Positioning in the existing system is the focus of many scholars. The pseudo-range single-point is the base of the positioning system, and it is positioning fast, easy to implement and has no integer ambiguity, so it has great research and application value.As a global optimization algorithm,particle swarm optimization (PSO) algorithm can play a good role in the process of the Compass pseudo-range positioning. In the pseudo-range single-point positioning, the traditional least square method (LS) has low accuracy because of the linearization error and strong dependence on initial value. In this paper, by analyzing the advantages and disadvantages of the least square method and the PSO, a new algorithm LS-PSO is proposed. Firstly,we use the least square method to locate the approximate position of the receiver as the initial value of the PSO algorithm in order to set up the search space. Then the global optimal value is obtained by using the PSO. After the experimental verification, the LS-PSO algorithm can quickly converge to the optimal solution and effectively improve the accuracy of the Compass pseudo-range single-point positioning.Besides the improvement of the positioning algorithm, smoothing the original measurement data is another method to improve the positioning accuracy. The carrier-phase smoothing pseudo-range is one of the most commonly used algorithm. The commonly used carrier-phase smoothing algorithm is Hatch filter, Extended Kalman Filter (EKF), etc.Because the size of Hatch filter's smoothing window is fixed, the solution is easy to diverge in the continuous positioning, so it must be reasonable to select the size of the smoothing window. It has a good result when EKF has the precise error model, but the estimation of the error model is a difficult problem. Through the analysis of factors affecting Hatch smoothing window size, using the improved particle swarm optimization algorithm (IPSO) to optimize each epoch's smoothing window size, determine the optimal smoothing window size.Experimental results show that the combination of improved particle swarm optimization and Hatch filter (IPSO-HATCH) can effectively improve the filtering effect and positioning accuracy, and solve the problem of the Hatch filter window size selection.