This paper proposes two schemes for indoor positioning by fusing Bluetooth beacons and a pedestrian dead reckoning (PDR) technique to provide meter-level positioning without additional infrastructure. As to the PDR approach, a more effective multi-threshold step detection algorithm is used to improve the positioning accuracy. According to pedestrians’ different walking patterns such as walking or running, this paper makes a comparative analysis of multiple step length calculation models to determine a linear computation model and the relevant parameters. In consideration of the deviation between the real heading and the value of the orientation sensor, a heading estimation method with real-time compensation is proposed, which is based on a Kalman filter with map geometry information. The corrected heading can inhibit the positioning error accumulation and improve the positioning accuracy of PDR. Moreover, this paper has implemented two positioning approaches integrated with Bluetooth and PDR. One is the PDR-based positioning method based on map matching and position correction through Bluetooth. There will not be too much calculation work or too high maintenance costs using this method. The other method is a fusion calculation method based on the pedestrians’ moving status (direct movement or making a turn) to determine adaptively the noise parameters in an Extended Kalman Filter (EKF) system. This method has worked very well in the elimination of various phenomena, including the “go and back” phenomenon caused by the instability of the Bluetooth-based positioning system and the “cross-wall” phenomenon due to the accumulative errors caused by the PDR algorithm. Experiments performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building in the China University of Mining and Technology (CUMT) campus showed that the proposed scheme can reliably achieve a 2-meter precision.

Probabilistic neural network has successfully solved all kinds of engineering problems in various fields since it is proposed. In probabilistic neural network, Spread has great influence on its performance, and probabilistic neural network will generate bad prediction results if it is improperly selected. It is difficult to select the optimal manually. In this article, a variant of probabilistic neural network with self-adaptive strategy, called self-adaptive probabilistic neural network, is proposed. In self-adaptive probabilistic neural network, Spread can be self-adaptively adjusted and selected and then the best selected Spread is used to guide the self-adaptive probabilistic neural network train and test. In addition, two simplified strategies are incorporated into the proposed self-adaptive probabilistic neural network with the aim of further improving its performance and then two versions of simplified self-adaptive probabilistic neural network (simplified self-adaptive probabilistic neural networks 1 and 2) are proposed. The variants of self-adaptive probabilistic neural networks are further applied to solve the transformer fault diagnosis problem. By comparing them with basic probabilistic neural network, and the traditional back propagation, extreme learning machine, general regression neural network, and self-adaptive extreme learning machine, the results have experimentally proven that self-adaptive probabilistic neural networks have a more accurate prediction and better generalization performance when addressing the transformer fault diagnosis problem.

The integration of Global Navigation Satellite Systems (GNSS) carrier phases with Inertial Navigation System (INS) measurements is essential to provide accurate and continuous position, velocity and attitude information, however it is necessary to fix ambiguities rapidly and reliably to obtain high accuracy navigation solutions. In this paper, we present the notion of combining the Global Positioning System (GPS), the BeiDou Navigation Satellite System (BDS) and low-cost micro-electro-mechanical sensors (MEMS) inertial systems for reliable navigation. An adaptive multipath factor-based tightly-coupled (TC) GPS/BDS/INS integration algorithm is presented and the overall performance of the integrated system is illustrated. A twenty seven states TC GPS/BDS/INS model is adopted with an extended Kalman filter (EKF), which is carried out by directly fusing ambiguity fixed double-difference (DD) carrier phase measurements with the INS predicted pseudoranges to estimate the error states. The INS-aided integer ambiguity resolution (AR) strategy is developed by using a dynamic model, a two-step estimation procedure is applied with adaptively estimated covariance matrix to further improve the AR performance. A field vehicular test was carried out to demonstrate the positioning performance of the combined system. The results show the TC GPS/BDS/INS system significantly improves the single-epoch AR reliability as compared to that of GPS/BDS-only or single satellite navigation system integrated strategy, especially for high cut-off elevations. The AR performance is also significantly improved for the combined system with adaptive covariance matrix in the presence of low elevation multipath related to the GNSS-only case. A total of fifteen simulated outage tests also show that the time to relock of the GPS/BDS signals is shortened, which improves the system availability. The results also indicate that TC integration system achieves a few centimeters accuracy in positioning based on the comparison analysis and covariance analysis, even in harsh environments (e.g., in urban canyons), thus we can see the advantage of positioning at high cut-off elevations that the combined GPS/BDS brings.

In this paper, a scheme is presented for fusing a foot-mounted Inertial Measurement Unit (IMU) and a floor map to provide ubiquitous positioning in a number of settings, such as in a supermarket as a shopping guide, in a fire emergency service for navigation, or with a hospital patient to be tracked. First, several Zero-Velocity Detection (ZDET) algorithms are compared and discussed when used in the static detection of a pedestrian. By introducing information on the Zero Velocity of the pedestrian, fused with a magnetometer measurement, an improved Pedestrian Dead Reckoning (PDR) model is developed to constrain the accumulating errors associated with the PDR positioning. Second, a Correlation Matching Algorithm based on map projection (CMAP) is presented, and a zone division of a floor map is demonstrated for fusion of the PDR algorithm. Finally, in order to use the dynamic characteristics of a pedestrian’s trajectory, the Adaptive Unscented Kalman Filter (A-UKF) is applied to tightly integrate the IMU, magnetometers and floor map for ubiquitous positioning. The results of a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirm that the proposed scheme can reliably achieve meter-level positioning.