Study On Location And Prediction Model Based On Bootstrap Method

The location service industry has gradually developed along with the progress of the Internet and mobile internet in China.Driven by user demand,location services will be integrated with the surrounding environment,and location services will continue to be integrated into more entertainment,life and social user needs.In the current positioning system,GPS can provide meter-level positioning accuracy in an outdoor environment.However,in an indoor environment,under the influence of many factors such as building blockage and multi-path effects,it is difficult for GPS to perform position resolution.Therefore,wireless technology has become a research hotspot in indoor positioning.In wireless network communications,there are many terminal positioning technologies based on different positioning devices.These technologies can adapt to different positioning environments.This article studies the positioning technology in the NLOS environment.The main work is as follows:1.This paper presents the introduction and analysis of Time of Arrival(TOA)algorithm.Then compare the location results calculated with error-free data and data with Gaussian noise to explore the sensitivity of the traditional TOA method in a complex non-line-of-sight propagation environment.2.In the NLOS environment,an improved TOA algorithm is proposed.Through the introduction of Bootstrap sampling and Monte Carlo simulation method,the error is simulated and analyzed to obtain a more accurate error distribution.Then simulate the straight line propagation data.Based on this,the classical Newton method is used to achieve positioning of the mobile terminal.Empirical analysis shows that this improved TOA algorithm has higher accuracy and robustness.3.The TOA positioning algorithm uses the triangulation principle.Under the NLOS environment,the traditional positioning algorithm needs to process noise by using multiple base stations to simultaneously locate,which greatly increases the complexity of the algorithm.Therefore,the improved TOA algorithm proposed in this paper is optimized to reduce the number of base stations used in the positioning algorithm,and through experiments,it is feasible to improve the improved TOA algorithm.4.Based on the positioning technology and using the position data provided by satellites,this paper proposes a trajectory prediction method based on the Bayesian prediction method.We give its algorithm,and predict the next position using experimental data.Finally,the error analysis of the prediction results of this method is performed,The results show that this method has better prediction effect.