| Indoor Positioning Technology is a system method to realize location awareness in indoor environment,which is combined with various technologies like base station location,Inertial aided positioning and so on.With rapid development of wireless communication technique,people’s daily demand for indoor service based on positional information grows more and more strongly.High-precision Indoor Positioning Technology is key to get exactly positional information,which makes it become one of the hot topics of contemporary interest.This article will study and optimize typical indoor location fingerprinting positioning method based on Bluetooth 4.0 wireless technology owning to its delightful characteristics like low power,low cost and convenient deployment.There are two major parts in Indoor fingerprint positioning method.One is setting up a positional information database,the other one is real-time locating.To make better algorithm performance,traditional method has to be used firstly to cluster database’s information,then math it by KNN in positional stage,which can not only guarantee the location accuracy but also simply the algorithm computation.This article introduces PSO,which is to solve the initial value of FCM first,then aiming to weaken the deficiency in positional fingerprinting location technology,like too sensitive to initial value,easily trapped in optimal value,which can improve positioning location accuracy and robustness.At the meantime,gravity Lagrange interpolation is applied to solve the problem of the sampling cost in setting up database stage,which can reduce the cost but ensure the location accuracy.At the real-time location stage,the application of fuzzy decision theory can avoid KNN’s error and computing redundancy.Eventually,this article design and simulate these several optimized algorithms and the whole positioning method through Matlab platform.These simulations have compared the performance of location method before and after optimized,including the effect of producing by optimized interpolation for building database,the effect to cluster brought by optimized FCM algorithm,the whole location method’s comparison between before and after optimizing.At last,the experiment manifest that the optimized positioning algorithm has improved location accuracy and efficiency.Also the gravity Lagrange interpolation can cut down sampling cost observably with a little missing of location accuracy. |
