Research On Improved Combining Localization Algorithm Based On Kalman Data Fusion

High-precision positioning technology has been widely used in various engineering fields.Infrared positioning technology,radio frequency identification technology and other commonly used positioning technologies have limited application range due to harsh environmental requirements and other shortcomings.Inertial positioning technology is widely used because it does not rely on the external environment,but its positioning error will accumulate,positioning accuracy is limited.At present,Kalman filter algorithm has been widely used in combination positioning for data fusion,the accuracy of data fusion plays an important role in the accuracy of combination positioning,how to improve the accuracy of Kalman filter is an effective means to improve the accuracy of combination positioning.This paper focuses on data fusion in combined location.Two improved UKF filtering algorithms are proposed for data fusion in the combination of location data fusion.The first algorithm uses the harmonic algorithm to optimize the selection of parameter kappa in the UT transform of the UKF filtering algorithm.The parameter to be selected in UT transform affects the calculation of scaling factor,which affects the whole prediction error and brings the problem of low prediction accuracy.It can further improve the accuracy of the prediction error and thus improve the effect of the positioning accuracy to use the harmonic algorithm to optimize the selection of the selected parameters.The second algorithm uses the harmonic difference algorithm to optimize the selection of the selected parameters,so as to achieve the need to improve the positioning accuracy.Finally,two improved UKF filtering algorithms are simulated and verified by MATLAB simulation.It is verified that the two algorithms can effectively improve the accuracy of data fusion in combination location technology,and achieve the improvement of positioning accuracy.The improved UKF filtering algorithm is superior,feasible and effective compared with other algorithms by comparing the MATLAB simulation.