Tightly coupled GPS/INS (Global Positioning System/Inertial Navigation System) cycle slip detection and repair algorithm based on Bayesian compressive sensing

Abstract

The invention relates to a method for detecting cycle slips in positioning signals, and in particular to a tightly coupled GPS / INS (Global Positioning System / Inertial Navigation System) cycle slip detection and repair algorithm based on Bayesian compressive sensing. The algorithm consists of two parts, the first part is creating a tightly coupled GPS / INS navigation system positioning model, and also overcomes the defect that satellite navigation can be easily blocked by terrains and that INS navigation errors can be accumulated as time goes by, so that the respective advantages can be sufficiently exerted; the second part is constructing a sparse cycle slip detection and repair model, firstly inter-station differentiation and epoch differentiation are carried out on an original carrier observation quantity, so that a carrier phase double-difference model is obtained, secondly cycle slip signal detection based on compressive sensing is obtained on the basis of the double-difference model, and finally cycle slip signals are repaired. The algorithm can effectively decrease the error rate of GPS cycle slip detection and increase the accuracy of GPS cycle slip repair, so that the positioning precision of GPS differential positioning is increased, and the algorithm has a wide application prospect.

Description

technical field [0001] The invention relates to a cycle slip detection method in a positioning signal, specifically a GPS / INS tight combination cycle slip detection and repair algorithm based on Bayesian compressed sensing to improve the positioning accuracy of GPS differential positioning, and is widely applicable to weak signals and highly dynamic environment. Background technique [0002] In the process of GPS positioning, it will be affected by the observation environment, interference noise and multipath effects, and in the urban environment, the GPS signal is frequently interrupted by occlusion, and the frequent occurrence of cycle slips affects the efficiency of navigation. However, the navigation performance of INS is not affected by the observation environment, and it can output a smoother high-speed navigation solution, but the INS positioning error will accumulate over time, and it is usually necessary to use GPS observations for periodic corrections, combining GP...

AI Technical Summary

Problems solved by technology

[0003] The present invention combines GPS and INS tightly through Kalman filtering to simultaneously overcome the defects that GPS is easily affected by terrain and INS navigation errors accumulate over time, and can describe various errors and corrections more accurately

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