RESUMEN
With the widespread use of indoor positioning technology, various services based on this technology are beginning to be offered to consumers and industrial applications. In the case of logistics facilities, in addition to indoor and outdoor spaces, there are top-bounded spaces (TBSs): elongated areas that are covered with roofs or eaves on the upper parts of buildings. The sides of such spaces are open, and workers and forklifts work in these areas. Only a few studies have been conducted on positioning methods for this unusual environment, and the way by which Signal-to-Noise Ratio (SNR) of Global Positioning System (GPS) changes with the stay in TBSs is unclear. Therefore, we conducted preliminary experiments and confirmed that TBS dwellings are difficult to stably detect with existing methods due to the combination of satellites with variable and unchanged SNRs. In this study, we designed a simple processing flow for selecting satellites with high probabilities of changing SNRs by using the spatial characteristics of TBSs as parameters (height, depth, and side opening orientation). We propose a method to detect the stay in TBSs using the SNR change rates of the selected satellites. As a result of evaluation experiments with three TBSs, we successfully detected the stay in TBSs with about 30% higher probability than those of an existing method.
RESUMEN
In this paper, a new emergency positioning technique is proposed based on ad hoc GNSS/UWB (Global Navigation Satellite System/Ultra-Wideband) network. The main innovations of the program are reflected in two aspects. First of all, a unified coordinate frame for indoor and outdoor environments is constructed dynamically with GNSS/UWB integration. In the outdoor environments, the high accuracy positioning can be achieved with GNSS/UWB equipment. The high-accuracy indoor coordinate is obtained by measuring the range observations between adjacent network nodes and outdoor GNSS/UWB nodes, and the range information of the UWB network is transmitted to the cloud server center. A network adjustment algorithm is proposed to improve the positioning accuracy of the UWB network. Secondly, a UWB indoor location algorithm based on robust EKF (Extended Kalman Filter) is proposed. By analyzing the transfer characteristics of gross error in EKF model, a new robust EKF model is established. The model is constructed based on the statistical characteristics of redundant observation components and prediction residual. The robust equivalent gain matrix is constructed, and the robust positioning solution of UWB is obtained with iteration. The global test is carried out first to further improve the real-time operation efficiency. Finally, a field indoor and outdoor seamless positioning experiment was carried out to verify the effectiveness of the proposed algorithm. The results show that the positioning accuracy of UWB emergency network nodes (anchors) can reach 0.35 m. Based on the network, the positioning accuracy of the tag can reach 0.38 m by applying the improved robust EKF positioning algorithm, which is improved by 20.83% and 73.43% compared with standard EKF and least square method, respectively.
RESUMEN
In the era of mobile internet, Location Based Services (LBS) have developed dramatically. Seamless Indoor and Outdoor Navigation and Localization (SNAL) has attracted a lot of attention. No single positioning technology was capable of meeting the various positioning requirements in different environments. Selecting different positioning techniques for different environments is an alternative method. Detecting the users' current environment is crucial for this technique. In this paper, we proposed to detect the indoor/outdoor environment automatically without high energy consumption. The basic idea was simple: we applied a machine learning algorithm to classify the neighboring Global System for Mobile (GSM) communication cellular base station's signal strength in different environments, and identified the users' current context by signal pattern recognition. We tested the algorithm in four different environments. The results showed that the proposed algorithm was capable of identifying open outdoors, semi-outdoors, light indoors and deep indoors environments with 100% accuracy using the signal strength of four nearby GSM stations. The required hardware and signal are widely available in our daily lives, implying its high compatibility and availability.