| With the development of technology in today’s society,people’s demand for precise positioning is increasing day by day.Outdoor positioning includes Global Navigation Satellite System(GNSS)positioning and Location Based Services(LBS)positioning,which are integrated with each other,to meet people’s positioning needs in outdoor complex environments.Indoor positioning includes Wi-Fi,Bluetooth,Inertial Navigation System(INS),Ultra Wide Band and other positioning technologies to achieve accurate positioning of people in indoor environments.Among them,Ultra Wide Band(UWB)technology has the characteristics of frequency bandwidth,high precision,anti-multipath effect,low power consumption,strong anti-interference,and strong penetration,which has attracted wide attention and is a mainstream application of indoor positioning in the future.UWB’s comprehensive capability is higher than other commonly used indoor positioning technologies,but its shortcomings also exist.The UWB positioning system cannot measure the pose,the delay leads to poor real-time performance,and the system adaptability is poor.Under the influence of the non-line of sight(Non Line Of Sight,NLOS)environment,the system accuracy will be greatly reduced.And most of the current UWB algorithms often ignore the inertial motion generated when the label moves.In order to reduce the impact of NLOS and noise,various weights and Kalman filtering are alternatively introduced for trajectory prediction so that the solution results meet the expected conditions..In order to solve the above problems,this paper found in the research that INS can be effectively combined with UWB.INS is a highly independent positioning technology,which can solve the problem of poor accuracy of UWB in NLOS environment,and UWB can solve the error accumulation generated by INS.Therefore,in this design,on the premise of using the off-the-shelf UWB kit and six-axis acceleration sensor,in order to improve the accuracy of the UWB system in the Line Of Sight(LOS)environment and reduce the influence of the NLOS environment on the UWB positioning accuracy,a method is proposed.The algorithm combines UWB with INS:1)At the one-dimensional level,a dynamic adaptive correction iterative algorithm based on the least squares method is introduced to improve the ranging accuracy of a single base station/single tag;2)At the two-dimensional level,an ellipsoid trilateral positioning method is proposed,and inertial navigation is introduced on this basis,and the Kalman filter is used to predict the trajectory of the ultra-wideband and inertial navigation fusion algorithm to compensate for the actual movement of objects carrying tags.inertial displacement;3)The coefficient shielding of UWB positioning accuracy by various occlusions that may appear in the indoor environment is simulated,and a positioning strategy in a non-line-of-sight environment is formulated to compensate for the error caused by the impact of the NLOS environment on UWB.With a large number of simulation experiments using the algorithm in python,the design was finally implemented in a large office space.The average error in the LOS environment was 2.15 cm to 2.43 cm.Compared with the single trilateral positioning method,it was reduced by 3.38 cm to 4.89 cm.About 48.4% to 63.1%.Compared with the Chan trilateral positioning method,the ellipsoid trilateral positioning method is improved by 0.91 cm to1.45 cm,and the performance is improved by about 17.0% to 40.3%.In the NLOS environment,the average error of this design algorithm is 6.36 cm to 8.11 cm,which is 3.7cm to 4 cm smaller than that of the algorithm without NLOS positioning strategy,and an increase of about 20.1% to 49.0%;compared with the Chan trilateral positioning method It has increased by 0.51 cm to 1.17 cm,an increase of about 7.8% to 15.5%.Moreover,the variance and the maximum error peak value have been greatly improved,the system stability has been improved,and the accuracy of the UWB indoor positioning system has been greatly improved. |
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