Research And Realization Of Match/Filtering Algorithm In Inertial/Gravity Navigation System

The capacity of longterm-underwater navigation has always been pursued by underwater vehicle as a major goal. Hidden is the premise of survival and effectiveness of underwater weapons(e.g. submarines ). As the INS(inertial navigation system) has a passive and autonomous character, INS create the conditions for the realization of self-hidden locating of underwater vehicle. Therefore, it has been the core-devices of navigation system of underwater vehicle (e.g. submarines) for a long time. However, the main shortcomings of INS is its position error accumulates over time, so it is unable to meet the longterm and high-precision navigation and positioning requirements. Under normal circumstances, in order to inhibit the growth of location error of the navigation system and improve longterm accuracy of navigation, INS must be carried out on correction regularly.Vehicle is required to be close to the surface in traditional methods, thus the possibility of exposure increases. This has a direct impact on the concealment or even threatens its survival. Therefore, a method in which vehicles can amend INS without being close to the surface is in need.Gravity-assisted navigation with the characteristics of high accuracy , no time limit, no need to extend out of the water and no external radiation, can solve the problem of submarine hiding. Navigation gravity applies to regions with larger changes in the geographical features in aid of INS.This paper analyzes the match-positioning algorithm (ICCP algorithm) which is often used in the inertia/gravity navigation. Based on the ICCP algorithm, we propose two improved ICCP algorithm in order to limit the error of INS , stored gravity map and gravity sensor. Based on the analysis of the extended Kalman filter-matching method, we propose a nonlinear filter algorithm. This paper describes the basic steps of gravity-matching navigation using UKF algorithm. We also propose an algorithm which combines match-positioning algorithm and recurrence Kalman filter algorithm.Finally, we simulate all the algorithms above and analyse simulation results.