A Gravity Matching Navigation Algorithm Based on Constraint

Gravity navigation algorithm is of great significance to improve matching efficiency and accuracy, and it is a hot issue in matching navigation technology. A new gravity matching navigation algorithm based on constraints correlation theory is put forward by analyzing the algorithm of INS/gravity matching integrated navigation. Based on the existing algorithms, the new algorithm increases valid information according inertial navigation systems by comparing the track direction and sailing distance, it can effectively eliminate a large number of interference matching tracks, improves matching efficiency and matching accuracy. The simulation results show that the matching accuracy of this algorithm is significantly improved compared with the probabilistic neural network method, and the accuracy is improved from kilometer to hectometre. The new algorithm shortens about 50% time for probabilistic neural network, greatly improves the efficiency of gravity matching, and better content the requirements of underwater navigation.