Research And Implementation Of Indoor Location Method Based On RSSI Probability Distribution And Virtual AP

With the rapid development of communication technology,Location Based Service(LBS)has completely changed people’s daily lifestyle.The emergence of applications such as Google Maps,Baidu Maps,and Gaode Maps has greatly benefited people in travel,navigation,search,and other aspects.However,due to the relatively weak power of satellite signals,it is difficult to penetrate obstacles,and the difficulty of locating indoor targets has increased.This is particularly true in environments with buildings and other spatial obstacles,where errors can be significant and inconvenient for users.Therefore,indoor positioning has emerged.Indoor positioning technology aiming to locate various indoor scenes has been widely used in military,power generation,healthcare,chemical manufacturing,transportation logistics,and catering services.Various indoor positioning technologies and methods have emerged during the development of indoor positioning.Indoor positioning technologies are mainly divided into the following categories:geomagnetic positioning technology,wireless sensing positioning technology,optical positioning technology,and ultrasonic positioning technology.Indoor positioning methods are mainly divided into geometric ranging and fingerprint positioning.The geometric ranging method is simple to calculate and easy to implement,but it is susceptible to multipath effects and cannot accurately locate the target.The core of fingerprint positioning is to establish a database of location fingerprints.When determining the location,the received signal information is compared with the fingerprint information in the database to determine the current location.With the application of various machine learning and deep learning methods in recent years,the indoor positioning accuracy based on fingerprint positioning methods has become increasingly high.This article mainly explores and implements a high-precision and stable indoor positioning method based on fingerprint positioning,with ZIGBEE technology as the basis method.The main contributions of this article are as follows:(1)The RSSI(Received Signal Strength Indicator)probability distribution model is constructed,and detailed analysis and experiments are conducted.Then,various similarity matching methods,such as EMD distance,JS divergence,and KL divergence,are compared based on the RSSI probability distribution to evaluate their impact on positioning accuracy.(2)Based on the distance attenuation model of signal propagation,the RSSI spatial resolution is analyzed,and it is found that RSSI signals of different strength intervals have inconsistent spatial resolution.On this basis,a dynamic weighting scheme is proposed,and a DWP(Dynamic Weight Positioning,DWP)algorithm based on RSSI probability distribution is presented.(3)Combining virtual AP and HMM(Hidden Markov Model),a VAP-HMM positioning algorithm is proposed to address the shortcomings of the DWP algorithm,which has a slightly larger maximum positioning error and does not consider the continuity of target position during motion.(4)An indoor positioning system is designed and implemented,and the proposed algorithms are used for positioning.The system performs well in terms of real-time and accuracy in positioning.