# A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
## 1 Introduction  
Compared with the outdoor open free space, the indoor channel environment and spatial topology are more complex, and the GNSS space signal service cannot be effectively covered in the indoor environment due to the attenuation of building occlusion. A pseudolite (PL) is a ground navigation transmitter that can transmit GNSS-compatible signals. In an indoor environment, pseudolites transmit the signals similar to the space satellite signals to the user terminal through the transmitting antenna. For location prediction, indoor and outdoor continuous positioning services can be provided through software upgrades without changing the existing hardware of smart terminals on the market [1, 2, 3]. However, in the indoor environment, the pseudolite signal will produce propagation effects such as path loss and multipath fading under the influence of multipath propagation and shadow shading. The mature theoretical methods of traditional GNSS positioning are not fully applicable for indoor pseudolite positioning. The well-known distance-based localization techniques, such as Time of Arrival (TOA), Time Difference of Arrival (TDOA), and Angle of Arrival (AOA) [4], do not work well in complex indoor environments. Fingerprint-based localization methods are not limited by line-of-sight (LOS), and have the advantages of low cost, high accuracy, and good stability, so they are widely used. The pros and cons of different indoor localization technologies such as AoA, ToF, RTOF, and RSS are discussed in reference [5], and several common fingerprint matching methods are also introduced in this article. Fingerprint localization has been extensively studied by researchers. An indoor localization approach is presented in reference [6], which utilizes the magnetic data from smartphone magnetic sensors to localize a pedestrian. The authors train a convolutionalneural network to recognize the indoor scene, and build a database of magnetic field patterns to lower the device dependence. Reference [7] proposes a method using the intersection area of AP to calculate the user's position, which alleviates the influence of human body loss on the positioning accuracy and reduces the device's dependence on the fingerprint database. Reference [8] proposes an indoor fingerprint positioning sensor fusion framework that combines Wi-Fi RSSI signals, smartphone sensors, and the PDR algorithm, which shows reasonable localization accuracy with fewer IMU sensor errors. The authors of [9] propose an algorithm that utilizes high-level features extracted by deep learning, extreme learning machines, and autoencoders to improve localization performance in feature extraction and classification, while increasing the amount of training data to improve localization performance. The authors of [10] use the K-Nearest Neighbor (KNN) algorithm to study the accuracy of wireless fingerprint localization and compare its performance with other fingerprint localization algorithms. Reference [11] proposes an indoor positioning algorithm that combines fingerprint positioning and dynamic prediction. The proposed algorithm can alleviate the influence of received signal strength (RSS) fluctuations and has better positioning accuracy and stability. In addition to the above-mentioned RSS, the data form currently obtained based on indoor positioning fingerprints also includes channel state information (CSI). RSS represents the signal strength value superimposed at the receiving point. When it is used as the only fingerprint feature, much useful information such as phase are ignored, so that the propagation characteristics of the signal in the channel are not well-reflected. RSS can only achieve ideal results in some simple environments, and CSI is more stable than RSS in traditional indoor environments. CSI contains both amplitude and phase information, and can provide richer frequency domain information than single-valued RSS.