Indoor High-precision Positioning Algorithms And Optimization In Non-line-of-sight Environment

High precision positioning technology not only plays an important role in the military field,but also is indispensable in production and life.It has been widely used in traffic navigation,intelligent driving,tracking and monitoring,and peripheral service acquisition and push.According to statistics,people spend more than three-quarters of their time in an indoor scene.However,due to the large number of walls and obstacles in the indoor scene,the radio signal has significant multipath effects,resulting in the measurement value containing NLOS(Noneline-of-sight)errors,which has a great impact on positioning accuracy,and it is difficult to meet the indoor high-precision positioning requirements.NLOS phenomenon is a difficult problem in indoor positioning.It is widespread in indoor environments and has become a problem that restricts the development of indoor positioning.Therefore,this article studies positioning algorithms in non-line-of-sight environments to meet the needs of indoor highprecision positioning.The main work of the paper is as follows:First,the generation of indoor positioning errors is analyzed.Aiming at the NLOS problem in indoor positioning,this paper proposes a NLOS iterative correction positioning algorithm suitable for non-line-of-sight environments.This algorithm can solve the closed solution of the positioning result and the NLOS error,correct the NLOS error of the measured value and then locate again,and suppress the influence of the NLOS error through continuous iteration.Then a NLOS recognition method is proposed,which is integrated with the proposed NLOS iterative correction positioning algorithm to achieve high-precision positioning.Secondly,for the positioning of moving targets in NLOS environment,this paper proposes a hybrid particle filter positioning algorithm suitable for non-line-of-sight environments.The distributed particle filter positioning algorithm is used to deal with the situation that the proposed NLOS recognition algorithm cannot identify,the reliable LOS measurement value is used to calculate the residual as the weight,and the positioning results of each local filter are combined in a weighted manner.Taking into account the problem of particle depletion,the maximum likelihood location algorithm is combined with centralized particle filter to detect,and the filter is restarted when particle depletion is detected.Finally,relying on the virtual space high-precision indoor positioning simulation platform,simulation experiments are carried out to test and verify the performance of the proposed NLOS iterative correction positioning algorithm and the hybrid particle filter positioning algorithm in a non-line-of-sight environment.Experimental results prove that the NLOS iterative correction positioning algorithm has smaller positioning errors than the existing Chan algorithm and residual weighting algorithm in a non-line-of-sight environment.The proposed hybrid particle filter positioning algorithm,Compared with Kalman filter and particle filter positioning algorithm,it has higher positioning accuracy.