Research On The Key Technologies Of Underwater Gravity Field Aided Navigation And Positioning

Due to the problem of INS position error accumulation, which is not possible to meetlong-term and high-precision navigation needs for the submarines and other underwatervehicles. So as to improve the navigation accuracy and obtain the high reliability locationinformation, the INS error must be periodically revised. As a result, the gravitational fieldaided navigation and positioning system came into being. The system is still in theexploratory stage, a number of key issues need to be further resolved and improved. Thegravity field aided navigation and positioning technology is as the research background inpaper, in order to improve the submersible navigation accuracy and reliability as the goal, thekey technology is researched, which is involved with the gravity gradient reference mapconstruction, the decision area determine and matching solution algorithm improved and soon.Firstly, the structure of the traditional gravitational field aided navigation and positioningsystem is studied in-depth. On the basis, the importance of each component part is analyzed.According to the problem that geological density model can not be obtained in gravitygradient forward process, the existing data sources are used to simulate the geological densityand applied to gravity gradient forward algorithm of the variable density. A method ofcalculating geological density based on high precision gravity anomaly data and digitalelevation data is given in the least squares estimation method. The method is combined withthe rectangular prism method to analyze the influence of the residual density on the gravitygradient forward algorithm. Simulation results show that the digital elevation is still the mainfactor that affects the accuracy of gravity gradient forward algorithm. In the gravity gradientforward process, the land area should consider applying the local average density, theunderwater area can apply the residual density (1.643g/cm3).According to the problem that the error ellipse method is not very intuitive to expressdecision area (search area) performance, and is not convenient to program implementation,the error ellipse equation expression is derived by the direct method and coordinatetransformation method. The relationship is discussed in detail between the error ellipse andthe conventional rectangular method to the "3σ " principle, the conclusions are obtained thaterror ellipse tangent rectangle coincides with conventional rectangular area when INS easterror standard deviation is greater than the north standard deviation, and error ellipse tangentrectangle and conventional rectangle are y=xsymmetrical when the INS east error standard deviation is less than the north standard deviation. In view of low and middle precision INSthe three simulation experiments are carried out, results show that the error ellipse is tangentto the conventional rectangular, at the same time, each real location points are contained in theerror ellipse area, thus the decision area method of determineing with the error ellipse iscorrect.The contour algorithm is derived in detail and analyzed in the correlation matchingalgorithm, on the basis, contour algorithm is studied on the correlation problem of therotational and translational transformation process. The problem is found that rotation andtranslation calculation has strict order, which inevitably rotation matrix calculation errorreacts on the translation vector, the mixing error enhance is caused. In order to solve theproblem, a closest point encryption contour improved algorithm is proposed. And thesimulation experiments are carried out, results show that the improved method is effective, thepositioning accuracy is better than the traditional contour algorithm when the initial matchingerror is small.In order to improve the navigation accuracy of the gravity anomaly aided navigationalgorithm in large initial matching error, probabilistic neural network matching algorithm isdiscussed in detail. According to the defect that the algorithm is too dependent on INS track, aprobabilistic neural network matching algorithm based on the track rotary trimming isproposed. The algorithm consists of four parts: track initiation, track probability roughcompetition, track probability sperm competition and track probability final competition, andthe algorithm is with automatic adjustment and geting the best matching track feature.Respectively the simulation experiments are carried out in four groups typical characteristicsgravity anomaly area, results show that the improved probabilistic neural network algorithmis able to achieve higher matching rate. The defect can be overcome that the conventionalalgorithm depends on the INS track and the characteristics is inherited that conventionalalgorithm is still able to work well in the large initial matching error.In order to overcome the defect that navigation accuracy of gravity gradient aidednavigation algorithm is not high in large initial matching error, the probabilistic neuralnetwork algorithm is introduced to gravity gradient aided navigation. At the same time, aimproved contour method is proposed that probabilistic neural network algorithm is used fortuning the initial position: in the decision area, probabilistic neural network algorithm is usedfor tuning the initial matching position to reduce INS error, and the matching track is formed.On this basis, the best matching position is got by contour algorithm. Respectively thesimulation analyses are done in different initial conditions, the conclusion is got that the probabilistic neural network algorithm is not easily divergence in the large initial error butnavigation accuracy is not high. Simulation experiments are carried out with the improvedmethod in the initial error3.720′、4.617′and5.438′, results show that the improved methodis still able to achieve a high navigation accuracy even in the large initial error.