In order to solve the problem of pedestrian positioning in the indoor environment, this paper proposes a high-precision indoor pedestrian positioning system (HPIPS) based on smart phones. First of all, in view of the non-line-of-sight and multipath problems faced by the radio-signal-based indoor positioning technology, a method of using deep convolutional neural networks to learn the nonlinear mapping relationship between indoor spatial position and Wi-Fi RTT (round-trip time) ranging information is proposed. When constructing the training dataset, a fingerprint grayscale image construction method combined with specific AP (Access Point) positions was designed, and the representative physical space features were extracted by multi-layer convolution for pedestrian position prediction. The proposed positioning model has higher positioning accuracy than traditional fingerprint-matching positioning algorithms. Then, aiming at the problem of large fluctuations and poor continuity of fingerprint positioning results, a particle filter algorithm with an adaptive update of state parameters is proposed. The algorithm effectively integrates microelectromechanical systems (MEMS) sensor information in the smart phone and the structured spatial environment information, improves the freedom and positioning accuracy of pedestrian positioning, and achieves sub-meter-level stable absolute pedestrian positioning. Finally, in a test environment of about 800 m², through a large number of experiments, compared with the millimeter-level precision optical dynamic calibration system, 94.2% of the positioning error is better than 1 m, and the average positioning error is 0.41 m. The results show that the system can provide high-precision and high-reliability location services and has great application and promotion value.

In view of the inability of Global Navigation Satellite System (GNSS) to provide accurate indoor positioning services and the growing demand for location-based services, indoor positioning has become one of the most attractive research areas. Moreover, with the improvement of the smartphone hardware level, the rapid development of deep learning applications on mobile terminals has been promoted. Therefore, this paper borrows relevant ideas to transform indoor positioning problems into problems that can be solved by artificial intelligence algorithms. First, this article reviews the current mainstream pedestrian dead reckoning (PDR) optimization and improvement methods, and based on this, uses the micro-electromechanical systems (MEMS) sensor on a smartphone to achieve better step detection, stride length estimation, and heading estimation modules. In the real environment, an indoor continuous positioning system based on a smartphone is implemented. Then, in order to solve the problem that the PDR algorithm has accumulated errors for a long time, a calibration method is proposed without the need to deploy any additional equipment. An indoor turning point feature detection model based on deep neural network is designed, and the accuracy of turning point detection is 98%. Then, the particle filter algorithm is used to fuse the detected turning point and the PDR positioning result, thereby realizing lightweight cumulative error calibration. In two different experimental environments, the performance of the proposed algorithm and the commonly used localization algorithm are compared through a large number of experiments. In a small-scale indoor office environment, the average positioning accuracy of the algorithm is 0.14 m, and the error less than 1 m is 100%. In a large-scale conference hall environment, the average positioning accuracy of the algorithm is 1.29 m, and 65% of the positioning errors are less than 1.50 m which verifies the effectiveness of the proposed algorithm. The simple and lightweight indoor positioning design scheme proposed in this article is not only easy to popularize, but also provides new ideas for subsequent scientific research in the field of indoor positioning.

Measurement bias due to non-line-of-sight (NLOS) reflections and indistinguishable multipath fading can be collectively referred to as channel bias, which degrades the performance of the iterative localization algorithms and is the main reason limiting the performance of range/angle-of-arrival (AoA) based UWB localization systems. In order to build an adaptive and generalized indoor UWB localization system, channel bias and the corresponding means of compensation and elimination need to be investigated. In this paper, we provide an indoor UWB localization method based on adaptive channel bias estimation to compensate for distance and angle measurement errors. By means of a Dirichlet Process Gaussian Mixture Model (DPGMM) based on variational inference, the channel bias model for each anchor can be built without actual position calibration, and furthermore a maximum a posteriori (MAP) estimator for the tag location is designed. Test results in open indoor environments and complex indoor environments show that our method performs comparably in static line-of-sight (LOS) cases and better in NLOS and dynamic cases compared to conventional single-anchor localization method.

At present, the indoor positioning method of pseudolite based on fingerprint is restricted by the range and density of fingerprint collection, and its stability, reliability and accuracy are still technical bottlenecks. Aiming at the shortcomings of existing indoor fingerprint positioning, combined with the advantages of deterministic ray tracing simulation, a new hybrid method of measurement and simulation is proposed to generate the fingerprint feature input set to drive the position prediction model. The feature parameters generated by the simulation calculation are added to the neural network as part of the input, the original artificial neural network structure is improved, and the sample features of the neural network input data set are expanded. Taking the indoor arrival hall of an airport as an example, the static and dynamic positioning tests are carried out by combining the simulation and measured fingerprint construction methods. Compared with the traditional positioning effect based on fingerprint collection alone, the static positioning error of the proposed method is reduced by 42.2%, and the dynamic positioning error is reduced by 46.9%.

Aiming at the problem that commercial navigation chips cannot provide positioning in GNSS signal blocking environments, an indoor high-precision positioning system based on homologous array pseudolite was designed. By deploying pseudolite base stations in indoor environments, the existing commercial navigation terminals on the market can be transformed from “unlocatable” indoors to “locatable” without changing the hardware. An interepoch carrier phase difference positioning method based on nonlinear filtering is proposed. By introducing a pseudorange similarity estimation model, it overcomes the problems of poor pseudorange measurement accuracy and integer ambiguity solution and achieves decimeter-level positioning in indoor environments. To address the issue of divergent positioning results caused by factors, such as differences in target movement speed and carrier phase cycle slip, a Doppler frequency shift-assisted dynamic update of state transition equations and CPDD cycle slip detection method are proposed, further improving the continuity and accuracy. The positioning test results in microwave anechoic chamber and typical indoor environments show that over 85% of the positioning errors within the coverage area are less than 1 m, with an average positioning error of about 0.33 m. Compared with the more advanced Cd search and Z-KPI algorithms in this field, the positioning accuracy is improved by more than 80%.

At present, the interaction mechanism between the complex indoor environment and pseudolite signals has not been fundamentally resolved, and the stability, continuity, and accuracy of indoor positioning are still technical bottlenecks. In view of the shortcomings of the existing indoor fingerprint positioning methods, this paper proposes a hybrid CSI fingerprint method for indoor pseudolite positioning based on Ray Tracing and artificial neural network (RT-ANN), which combines the advantages of actual acquisition, deterministic simulation, and artificial neural network, and adds the simulation CSI feature parameters generated by modeling and simulation to the input of the neural network, extending the sample features of the neural network input dataset. Taking an airport environment as an example, it is proved that the hybrid method can improve the positioning accuracy in the area where the fingerprints have been collected, the positioning error is reduced by 54.7% compared with the traditional fingerprint positioning method. It is also proved that preliminary positioning can be completed in the area without fingerprint collection.