Research On NLOS Identification And Compensation Technology In High Precision Ranging System

High precision ranging technology is of great significance for positioning systems,especially the research of ranging technology in complex channel environments.In complex ranging environment,the probability of signal propagation being blocked by non-line of sight(NLOS)barriers is greatly increased,and the signal is difficult to be effectively detected due to obstacle blocking,resulting in a large NLOS ranging error.How to compensate NLOS ranging error is one of the hot research issues in high precision ranging technology.Therefore,this paper focuses on the NLOS recognition and error compensation techniques in high precision ranging systems.In NLOS ranging system,the existing methods for ranging error compensation are mainly divided into two categories: ranging error compensation algorithm based on NLOS channel identification and NLOS error elimination.In order to compensate the NLOS ranging error,this paper first proposes a NLOS channel identification method based on fuzzy correlation analysis,which uses skewness,kurtosis,Rice coefficient and K-S test value to identify the channel jointly.In this paper,the ranging results and the selected channel characteristic parameters are analyzed by grey correlation analysis,and the correlation coefficient is normalized as the weighting coefficient of the fuzzy comprehensive evaluation method.The channel is identified by comparing the fuzzy membership of LOS and NLOS channels.In addition,based on the traditional NLOS outlier abandonment method,Wylie-based LOS reconstruction method and the biased Kalman filtering method,this paper proposes a NLOS ranging error compensation method based on Huber-Kalman filtering.Firstly,the Huber linear regression error threshold is determined according to the NLOS channel identification results,and the Huber cost function is constructed and minimized.The NLOS ranging results are reconstructed by linear regression and then input into Kalman filter as measured values.After finite iterations,the outlier distribution can be effectively reduced and the ranging results can be more accurate.According to the simulation results,compared with the existing algorithms,the proposed method can further compensate the NLOS error and improve the ranging accuracy.In order to achieve real-time and high precision ranging in NLOS channel,this paper propose a NLOS error elimination algorithm based on clustering super-resolution detection method to recognize the first path signal.The generalized channel coefficients are estimated by the least square method,and the TOA estimation model of the first path is constructed to transform the TOA estimation into the binary detection of the channel coefficients.When the channel coefficients is estimated in the time domain,a fuzzy clustering method is utilized to adaptively divide it into channel impulse response and noise,and thus the TOA of first path can be estimated.Due to the limitation of bandwidth and sampling rate on high precision ranging system,the super-resolution ranging problem is further considered in this paper.By defining the modulus of vector summation function,the super-resolution TOA estimation problem is transformed into an unconstrained maximization problem,and the fractional delay is estimated by peak search method.The simulation results show that the proposed method using a low sampling rate can accurately identify the first path signal to estimate TOA in harsh NLOS environments,and achieve a decimeter-level ranging accuracy with high robustness.