Indoor Positioning Technology Research Based The Angle To Bluetooth 4.0 Station

With the development of modern science technology and internet technology, the outdoor location technology such as GPS has been grown and mature. People get convenient from outdoor location technology by using Google Map, Baidu Map and Gaode Map. With the proven outdoor location technology, we people can inquire about the nearby shops and service, and people can also get the best path to destination include the bus path, self-driving path and foot path. Now that the outdoor location technology can bring so much profit to people, the indoor location technology should also benefit people in some ways. So, it is important that to find out the indoor position of something.This paper introduce the background and the research status of indoor location technology. And then this paper also introduce the three most important experiment. We collected enough data by this experiments and get the angle between the phone and Bluetooth station by neural network and filter forecasting. Then we can calculate the X-Y coordinates of the phone. The full work is as below:1. Research data was collected through positioning experiments in various conditions, including the outdoor experiment with ideal environment, indoor experiment with ideal environment and indoor experiment with complex environment. In the indoor experiment with ideal environment, the method of function fitting was applied to positioning analysis with the discovery that the accuracy of the angle draw positioning reached 5° and the positioning accuracy reached 70% in this kind of condition. In the indoor experiment with complex environment, the function fitting method was no longer applicable, so the neural network method was applied to train the data, improving the 180cm positioning accuracy to 65%.2.Filtering prediction method was applied to revise the data and to improve the positioning accuracy. Two methods were mainly applied. The first method was the linear superposition of neural network and the basic Kalman filter method. In order to realize the filtering prediction to the output variable, the author took the vector of neural network as the observed variables of filtering prediction. The second method was to extend the neural network of the Kalman filter, using the extended Kalman filter to predicate and revise the weight matrix of the neural network. Both methods can improve the positioning accuracy in the complex environment, especially the linear superposition of neural network and the basic Kalman filter method which can improve the 180cm positioning accuracy to 70%.3. In this paper, we make the system of indoor location which is made of Android client, Python server and iBeacon Bluetooth station come true. The iBeacon Bluetooth station is a double antenna station and the Android client is represent a user and the Python server is the most important part of the system which process the calculating of X-Y coordinates.