Positioning Error Analysis And Optimization Of Location Fingerprint Based On WLAN

With the rapid development of mobile internet and the popularity of mobile devices,location-based services are widely used in industrial production and daily life.The WLAN-based location fingerprint positioning method has received extensive attention due to its advantages of relying on existing infrastructures and high scalability.However,this technology still suffers from problems such as limited positioning accuracy and high system computational complexity.In order to solve the above problems,at first,the effects of many different factors on WLAN-based location fingerprint positioning are analyzed through a large number of experiments.The experimental results show that the reduction of sampling interval,the increase in the number of samples,and the increase in the number of APs help reduce the positioning errors,and the number of APs has a significant effect on the positioning error,which lays the foundation for subsequent research in this paper.Secondly,the Cramer-Rao lower bound(CRLB)is used to measure the contribution of different access points(AP)to the positioning system.Then,a new AP selection algorithm is implemented,which not only helps to improve positioning accuracy,but also significantly reduces the amount of system calculations.Finally,a large number of experiments are carried out in real scenes,and the effectiveness of the AP selection algorithm proposed in this paper is verified by comparison with the maximum signal strength(MaxMean,MM)and random selection algorithm.Experimental results show that the probability of positioning error less than 4m by using the proposed AP selection algorithm is 90.3%,which is obviously better than the other two AP selection algorithms.In summary,the CRLB-based AP selection algorithm proposed in this paper has the advantages of high positioning accuracy,strong anti-interference and low computational complexity,and has a good application prospect and use value.