Research On Indoor Positioning Technologies Of Location Fingerprint Method Based On ZigBee

With the successful applications of global positioning system (GPS) onoutdoor positioning, the demands of indoor positioning services are getting shaper.Because of the low positioning cost and flexible realization, the locationfingerprint method (LFM) is becoming the research focus in the existing indoorpositioning techniques. However, the time variation of indoor wireless signalmakes it difficult for LFM to realize pinpoint. So, improving the positioningperformance and practicability of LFM in the complex indoor environment notonly has important theoretical value, but also becomes the key of accelerating thetransition from theoretical study to application. In this paper, related techniques ofLFM in the two positioning stages are studied in the ZigBee network. Based onanalyzing research status and development of LFM at home and abroad, it ispointed out that the problem of construction workload of location fingerprintdatabase, the problem of matching efficiency of location fingerprints and theproblem of indoor positioning and tracking realizations are the three mainproblems of LFM. And then, starting with analyzing signal strength characteristics,methods for resolving these problems are proposed respectively.In order to analyze the data characteristics of signal strength as the scenefeature, the manifestations and causes of characteristics are studied by theoreticalanalysis and experimental verification with actual ZigBee signal strength samples.And then, a probability model is given to estimate the average positioning error ofLFM. Based on the model, the relationships between positioning performance ofLFM and space of sampling locations and numbers and locations of network accessdevices are studied, which can provide theoretical guide for configuring the relatedparameters reasonably during the positioning system deployment.In order to decrease the sampling workload of signal strength in the off-linestage, a method for constructing location fingerprint database is proposed based onthe spatial variability theory. A typical construction procedure of signal strengthsample variogram is given according to the actual signal strength samples. A fittingmethod of signal strength theory variogram is proposed based on the weightedleast square method. According to the continuity and variability of signal strengthat different locations reflected by trend and stochastic component of the mode, thebest linear unbiased estimation of signal strength is achieved by ordinary kriging.The experimental results show that the proposed method can get better estimationprecision of signal strength than weighted distance inverse method. The location fingerprint database constructed by it can decrease the sampling workloadefficiently and ensure the positioning precision at the same time.In order to improve the matching efficiency of location fingerprint, twoclustering methods for it are proposed based on the improved k-means algorithm.The methods raise the clustering accuracy by using the new further featureextraction methods of location fingerprint and elastic classification technologiesafter analyzing the problems of k-means algorithm application on locationfingerprint classification. FC-ID-FKM regards location fingerprint as a kind ofinterval-valued data and adopts fuzzy k-means algorithm to cluster it in the featurespace established by interval median and size. FC-KFKM summarizes locationfingerprint as a kind of interval-valued data obeyed normal distribution and adoptsfuzzy kernel k-means algorithm to cluster it in the feature space established bynormal distribution function determined by interval median and size. Theexperimental results show that the location fingerprint sets obtained by theproposed methods have remarkable clustering trends. They can all get betterresults on location fingerprint clustering than k-means.In order to realize the accurate positioning in the complex indoor environment,a Dempster-Shafer based indoor positioning method is proposed. The massfunction is constructed to allocate the belief and a pignistic probability basedevaluation index is given which can reflect the conflict degree on belief allocation.According to the index value, the evidence pretreatment method by belief discountis adopted to eliminate the fusion absurdity during positioning. To decrease thedecision risk on overlaped belief intervals, the distribution functions of focalelements are constructed according to the belief intervals and the probabilitiesranking in a descending order on union of belief intervals are calculatedrespectively which are taken as the basis of decision-making finally. Theexperimental results show that the proposed method has fast convergence and lowdecision risk. The positioning accuracy of the method is better than it of Bayesianinference on the high conflict degree. In order to realize the LFM based indoortracking, an improved particle filter tracking algorithm is put forward. The statespace model for tracking is established in the two-dimensional plane and theparticle weight is calculated according to signal strength estimation methodproposed in the paper. The experimental results show that the proposed method hasbetter tracking effect than KNN and Kalman filter.