Abstract:Ultra-Wide-Band technology has important application value for location services due to its high ranging accuracy and penetration performance. In the actual high-density positioning environment, the traditional positioning algorithm is affected by non-line-of-sight error and multipath effect, and it is difficult to accurately calculate the actual position coordinates in real time. Although increasing the number of base stations can effectively improve the accuracy of positioning, its cost is also increasing. Aiming at the problem of poor real-time performance and low positioning accuracy of ultra-wideband in high-density indoor positioning, an ultra-wideband positioning method based on support vector machine is proposed to improve the accuracy and robustness of positioning. A support vector machine model based on TDOA(TDOA ,Time Difference of Arrival) is given. The focus is on solving the problem of locating the classification problem. The support vector machine classification model is established by TDOA values and coordinate values. The one-to-one classification model is used to solve the coordinate values and improve the coordinate solution speed. The simulation results show that in the high-density real-time positioning, compared with the traditional Chan algorithm and Taylor algorithm, the method has higher real-time performance than the traditional algorithm when the positioning accuracy is similar, which satisfies the actual positioning, low power consumption, fast and high. Precision positioning requirements.