![]() ![]() A federated filter was utilized to estimate the state errors optimally. Secondly, the integration filter was divided into two subfilters: pseudorange subfilter and pseudorange rate subfilter. This step reduced the dimension of the state vector. Firstly, a difference between pseudorange measurements was determined, by which clock bias and drift were excluded from the integration state model. Two schemes were included in this design for reducing the computation load. In this paper, a new efficient filter model was proposed and evaluated. The two characteristics magnify the computation load of the tight and ultratight modes. ![]() In addition, compared with the loose integration mode, clock bias and drift are included in the integration state model. However, in the tight and ultratight modes, difference of pseudoranges and pseudorange rates from the GPS and SINS are employed to compose the measurement vector, in which the measurement vector dimension increases with the amount of available satellites. In the loose integration mode, position and velocity difference from the GPS and SINS are employed to compose measurement vector, in which the vector dimension has nothing to do with the amount of the available satellites. According to the data fusion strategies, the GPS/SINS integrated system could be divided into three different modes: loose, tight, and ultratight integration (LI, TI, and UTC). ![]() The GPS/SINS integration system could utilize the advantages of both the GPS and SINS and provide more reliable navigation solutions. The SINS is a totally self-contained navigation system which is hardly disturbed. The GPS has the advantage of providing precise navigation solutions without divergence, but the GPS signals might be blocked and attenuated. Global Positioning System (GPS) and strap-down inertial navigation system (SINS) are recognized as highly complementary and widely employed in the community. ![]()
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