# A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
## 1 Introduction
The positioning technology using the actual collected fingerprints based on a controlled experimental environment, the positioning service will not be able to be provided in the uncollected area; (2) In order to achieve full coverage of the indoor environment, multiple APs are needed as beacons, and the positioning accuracy is related to the density of test points. High-precision location service in large indoor venues means huge testing workload, and is greatly affected by a single acquisition error. Some researchers use the propagation model to calculate the virtual APs to reduce the deployment of the actual Aps [24; 25; 26; 27]. However, errors caused by simplifying different spatial propagation parameters are still inevitable. On the other hand, the fingerprint feature collection at the test point needs to actually measure the information of the surrounding APs, and the workload cannot be reduced accordingly. Some researchers use the existing sparse fingerprint database data to generate a dense fingerprint database by interpolation algorithm, but it may fail in some large scene areas (such as shopping malls, gymnasiums, etc.) since the signal features of these sparse reference points cannot represent the entire positioning area. Moreover, the running time of the algorithm will increase rapidly with the increase of the number of reference points, resulting in that the real-time performance of the positioning cannot be guaranteed. This paper proposes an indoor pseudolite simulated CSI fingerprint positioning method that combines measured data, deterministic calculation data, and an artificial neural network. The channel characteristic parameters obtained by the deterministic calculation method are used as the training set of the neural network together with the actual measurement data. The extended input set improves the positioning accuracy in the area where the fingerprints have been actually collected, and can also complete the preliminary positioning in the area where the fingerprints are not actually collected. The main work of this paper is summarized as follows:  
1. Selecting position-specific CSI parameters, and deriving the conversion relationship between the simulation parameters of the ray tracing method and the actual CSI parameters;  
2. An indoor pseudolite CSI fingerprint positioning method combining measured data, deterministic calculation data, and artificial neural network were proposed. On the basis of the original fingerprints input, the simulation feature parameters were added, expanding the sample features of the neural network input dataset. 3. Build an indoor test environment in the arrival hall of an airport, and verify the positioning performance of the proposed algorithm in a large indoor environment. The rest of the paper is organized in the following manner. Section 2 presents an overview of the indoor pseudolite fingerprint positioning method from the aspects of fingerprint feature selection and fingerprint positioning principle. Section 3 introduces the basic principle and calculation formula of the ray tracing method. Section 4 describes the proposed approach while Section 5 details the experiment setup and analyzes the results. Finally, conclusions are given in Section 6.