# A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
## 1 Introduction
In conclusion, CSI has better stability and location sensitivity than RSS, which provides a new idea for achieving a more robust and accurate indoor positioning effect. Reference [12] proposes an indoor fingerprint positioning method based on carrier phase difference, which uses GPS/BDS-compatible satellite signals transmitted by pseudolite base stations, compared with RSS fingerprints, the positioning accuracy is greatly improved. Reference [13] proposes a localization algorithm that combines RSS and CSI. The algorithm selects high-correlation RSS and CSI based on deep learning to build a fingerprint library, which improves the localization accuracy. Reference [14] proposes a localization method using autoregressive (AR) modeling entropy of CSI magnitude as a location fingerprint, which not only has the advantage of simple structure, but also retains location-specific statistical channel information. The experimental results show that the method significantly improves the localization performance. Reference [15] constructs a fingerprint database based on CSI values, further reduces its dimensionality by using a multi-dimensional scaling algorithm, and then uses KNN to obtain the estimated target location. Reference [16] proposes an EKF ranging scheme based on CSI and RSSI, which solves the ranging accuracy problem under high-load APs. The experimental results show that the method effectively improves the accuracy in an indoor environment. Two methods are widely used to obtain CSI, one is a statistical method based on actual channel measurement data, and the other is a mathematical method based on the accurate calculation of electromagnetic data. The first method is based on actual measurement data and has poor adaptability to different environmental characteristics, while the second method relies on ideal simplifications and assumptions about the environment, which brings an irreparable deviation from the actual situation. In general, deterministic methods are more accurate than statistical methods because they are not affected by a single acquisition error. But the deterministic model requires detailed prior environment information (3D map or scene model), resulting in high computational complexity. Fingerprint-based indoor positioning technology aims to find the functional relationship between eigenvalues and positions through known CSI information, and predict indoor positions online through matching technology. In a broad sense, neural networks can be introduced wherever there is a prediction, classification, or control problem [17; 18]. In reference [19], a deep neural network is used for indoor positioning based on the magnetic field. The neural network plays an important role in mitigating the influence of equipment heterogeneity and improving indoor positioning accuracy. As an efficient and accurate learning matching algorithm, neural networks can process fingerprint information to achieve more accurate positioning effects. This is due to two important advantages: the first is that the neural network is composed of interconnected neurons and can be used to model highly distributed and highly parallel problems; the second is that the neural network can learn functional relationships on the basis of the problem to be solved. At present, researchers usually focus on the improvement of network architectures and lateral comparisons of various machine learning methods [20; 21; 22; 23], and there are relatively few studies on the diversity and multidimensionality of neural network inputs. In summary, the CSI fingerprint localization method based on the neural network can more accurately describe indoor environment characteristics and realize real-time localization. However, most of the data sets used for training are actually collected data, and there are two major limitations as follows: (1) Location problem of unknown environment.