Research On Gravity Matching Algorithm For Underwater Navigation

Inertial navigation system(INS)is widely used in underwater navigation.The absolutely autonomous working mode makes the navigation system not be limited by the weather,the location and the outside interference.However,the INS error is accumulating with time and it can not meet the long sailing time navigation request.Therefore,the measure is necessary to restrain the navigation error divergence.As the development of gravity measurement technology,the gravity measurement accuracy is increasing.Since the gravity measurement is passive and the gravity information is stable,gravity aided inertial navigation becomes a hot issue in underwater autonomous navigation.Gravity aided inertial navigation system is consist of inertial navigation system,gravity measurement sensors,gravity reference map and matching algorithm.The accuracy of INS,gravity reference map and gravity measurement is determined by the device precision.And the advanced gravity matching algorithm can systematically improve the navigation accuracy.This thesis deals with the underwater vehicle’s autonomous navigation problem during long sailing time and the main point is studying the matching algorithm.In the background,the main content and innovation points are listed:A simple filter model is proposed.The state variable is vehicle position and the INS short-term output is employed to estimate a parameter in the model.Since the matching process is conducted with a gravity anomaly database tabulated in the form of a digital model,the restriction from resolution makes the filter model based on INS error equation invalid.In addition,point mass filter is more suitable for its low request of model accuracy and the variable number of position particles.Furthermore,a matching algorithm combined with filter recursive process and trajectory similarity transformation is proposed.The matching algorithm involves two-stage matching process.The first stage acquires a preliminary matching points via point mass filter and the second stage implements precise matching via sequence similarity transformation.In order to reduce the bad influence from large initial position error on the matching result of iterated closest contour point(ICCP)algorithm,a matching algorithm combined with ICCP algorithm and point mass filter is proposed.Firstly,point mass filter estimates instructional position points given in a large initial position error.Then,ICCP algorithm with sliding window is conducted for further matching.The simulation result proves that the combined algorithm can overcome the restriction from initial locating precision and STD of matching error decreases.Besides,a matching algorithm based on the terrain contour matching(TERCOM)algorithm is proposed.The algorithm applies the shortest path update mode to increase sampling frequency.Moreover,the positioning error is limited due to the correlation analysis method including navigation information.Compared to the conventional algorithm,the improved TERCOM algorithm possesses better performance.In the application,the matching accuracy is deeply affected by gravity anomaly data distribution.A rule of outlier detection is established to discover the mismatched points in a matching sequence.Due to the spatial particularity of adjacent position points in an INS trajectory,a spatial order constraint and an affine transformation error restriction is applied to detect the mismatched point pair.The simulation result proves that the mismatch diagnostic method can successfully finds out the mismatched point pair.A matching suitability analysis method based on density function estimate is proposed.Firstly,the density function is estimated and the value of matching suitability value is obtained by integrating process within a local area.Secondly,the areas in the reference map are evaluated by the value.The simulation test demonstrates that when the matching suitability density function is Gauss density function after pruning,the analysis result is reasonable and it can meet the request.The method makes the matching suitability analysis more comprehensive and analysis process is simplified as well.Besides,an area partition method based on region growing is proposed.The method selects the grid with the best matching suitability value as the seed and then all of the grids are traversed and classified.The simulation test indicates that the method can flexibly implement data cluster and typical regions extraction.