Research On Location Algorithm Based On WiFi Signal Fingerprint And Inertial Navigation

With the rapid development of wireless Internet technology,the demand for location based services is becoming more and more.Indoor positioning has attracted the attention of many scholars.There are a variety of indoor location methods.Among them,the methods based on WiFi signal intensity positioning and the use of inertial navigation for position tracking are favored by the vast number of researchers,WiFi signals Intensity positioning has the characteristics of low cost and low power consumption,but it is easily disturbed by the surrounding environment.Inertial navigation and positioning(PDR model)will not be affected by external environment,but there will be accumulated error.In view of the shortcomings of the above two methods,this paper improves the accuracy and reliability of indoor positioning by combining inertial navigation and WiFi fingerprint location.After reading a lot of references,this paper analyzes the causes of WiFi fingerprint and inertial navigation error,and proposes a method to predict pedestrian step estimation by using neural network algorithm.On this basis,a genetic algorithm is used to optimize the weights and thresholds of the neural network,so that the optimization of the neural network is optimized.The later algorithm has less error in the step size prediction.The WiFi signal fingerprint localization is usually based on the KNN nearest neighbor algorithm,but the algorithm has a low online location accuracy and a large accidental error after matching the database.In view of the large accidental error of the WiFi location,a particle filter algorithm based on the KNN is proposed to predict the particle state according to the weight value of the particle motion model.Finally,the two algorithms are combined to realize the positioning method of particle filtering and inertial navigation fusion.The final experimental positioning accuracy is obviously higher than the single algorithmThrough the experiment,the off-line signal is collected to simulate the data,and the performance of the optimized algorithm is verified.The experimental results show that the error of the optimized location algorithm is reduced.In order to verify the positioning effect of the two algorithm fusion positioning methods in practice,the software and hardware experimental environment is built,and the Android location acquisition signal APP is developed.The fusion algorithm proposed in this paper is applied to the system to realize the function of the indoor location estimation.The experimental platform in a typical indoor experiment verifies the positioning function of the fusion location algorithm through the experimental platform.The experimental results show that the algorithm can meet the basic requirements of indoor location.