Research On CSI Indoor Localization Methods Based On Improved Particle Filter

In recent years,due to the vigorous development of mobile network,wireless indoor positioning technology has brought a large number of location-based applications in life,trade and public utilities.The vigorous development of mobile and pervasive computing has aroused people’s strong demand for accurate and reliable indoor positioning schemes.Nowadays,indoor positioning using Wi Fi signal is an economical technology.In the current mainstream wireless signal monitoring,most indoor positioning systems use received signal strength(RSS).However,in complex cases,the positioning effect of these systems will be greatly affected due to some factors such as multipath effect.Different from RSS,the channel response from the physical layer can be used to solve the problem of multipath effect.Therefore,channel state information(CSI)is widely used to replace RSS in indoor positioning,but its complex structure leads to increased computational complexity.Therefore,location fingerprint,which not only has the simplicity of RSS,but also retains rich statistical location related information(such as CSI),is the research focus of indoor location technology.The research contents of this paper are as follows:(1)Aiming at the problem that the computational overhead of particle filter algorithm in preprocessing CSI information is too large,this paper proposes an adaptive variance and gradient particle filter(AVG particle filter)for resampling based on adaptive variance and state gradient.The improved particle filter algorithm is a backward recursive algorithm that depends on the whole CSI data set.By adjusting the value of variance and according to the gradient,it can generate particles close to the real location distribution or in the high probability region,so that more illogical CSI location samples(CSI location samples with smaller weight)can be deleted in the resampling stage,and the number of particles with larger weight can be increased,this reduces the computational overhead.(2)Aiming at the problems of large storage capacity of offline fingerprint database and high complexity of online fingerprint matching,this paper proposes an autoregressive modeling based on CSI amplitude entropy as location fingerprint.This entropy based channel enables us to perceive indoor statistical diversity from a new perspective.Because the massive measurement storage driven by accurate probability density function(PDF)estimation is avoided,this method not only maintains the structural simplicity of RSS,but also makes full use of the most location-specific statistical channel information.This simple fingerprint structure helps to reduce the complexity of pattern matching,and its informative statistical examples also help to improve the accuracy of location estimation.Finally,the proposed location method is verified by experiments.The proposed AVG particle filter algorithm and autoregressive modeling based on CSI amplitude entropy are used as location fingerprints to test its effect.The experimental scenes used in this paper are close to life.The reference points are set in these positions.Compared with some existing positioning methods,it is verified that the positioning algorithm and fingerprint data processing method proposed in this paper are effective.