RSSI wireless signal is a reference information that is widely used in indoor positioning. However, due to the wireless multipath influence, the value of the received RSSI will have large fluctuations and cause large distance error when RSSI is fitted to distance. But experimental data showed that, being affected by the combined factors of the environment, the received RSSI feature vector which is formed by lots of RSSI values from different APs is a certain stability. Therefore, the paper proposed RSSI-based fingerprint feature vector algorithm which divides location area into grids, and mobile devices are localized through the similarity matching between the real-time RSSI feature vector and RSSI fingerprint database feature vectors. Test shows that the algorithm can achieve positioning accuracy up to 2–4 meters in a typical indoor environment.

When the occupant density of buildings is large enough, evacuees are prone to congestion during emergency evacuation, which leads to the extension of the overall escape time. Especially for multi-exit buildings, it’s a challenging problem to afford an effective evacuation plan. In this paper, a novel evacuation planning algorithm applied to multi-exit buildings is proposed, which is based on an indoor route network model. Firstly, evacuees are grouped by their location proximity, then all groups are approximately equally classified into several evacuation zones, each of which has only one safe exit. After that, all evacuation groups in the same zone are sorted by their shortest path length, then the time window of each evacuation group occupying the safe exit is calculated in turn. In the case of congestion at the safe exit, the departure time of each evacuation group is delayed in its arrival order. The objectives of the proposed algorithm include minimizing the total evacuation time of all evacuees, the travel time of each evacuee, avoiding traffic congestion, balancing traffic loads among different exits, and achieving high computational efficiency. Case studies are conducted to examine the performance of our algorithm. The influences of group number, group size, evacuation speed on the total evacuation time are discussed on a single-exit network, and that of partitioning methods and evacuation density on the performance and applicability in different congestion levels are also discussed on a multi-exit network. Results demonstrate that our algorithm has a higher efficiency and performs better for evacuations with a large occupant density.

For existing wireless network devices and smart phones to achieve available positioning accuracy easily, fingerprint localization is widely used in indoor positioning, which depends on the differences of the Received Signal Strength Indicator (RSSI) from the Wireless Local Area Network (WLAN) in different places. Currently, most researchers pay more attention to the improvement of online positioning algorithms using RSSI values, while few focus on the MAC (media access control) addresses received from the WLAN. Accordingly, we attempt to integrate MAC addresses and RSSI values simultaneously in order to realize indoor localization within multi-story buildings. A novel approach to indoor positioning within multi-story buildings is presented in this article, which includes two steps: firstly, to identify the floor using the difference of received MAC addresses in different floors; secondly, to implement further localization on the same floor. Meanwhile, clustering operation using MAC addresses as the clustering index is introduced in the online positioning phase to improve the efficiency and accuracy of indoor positioning. Experimental results show that the proposed approach can achieve not only the precise location with the horizontal accuracy of 1.8 meters, but also the floor where the receiver is located within multi-story buildings.

The ambiguity resolution (AR) and validation of the global navigation satellite system (GNSS) have been challenging tasks for some decades. Considering the reliability problem of extra-wide-lane (EWL) ambiguity in the triple-carrier ambiguity resolution (TCAR), a method for validating the reliability of the EWL ambiguity using a single epoch was proposed for the BeiDou Navigation Satellite System (BDS). For the initial EWL ambiguity, obtained using a rounding estimator with a geometry-free (GF) model, the double-difference ionospheric delay was first estimated to construct a relative positioning model with an initial fixed ambiguity. Second, based on the theory of gross error detection and the AR characteristics of EWL, the second-best ambiguity candidate was constructed. Finally, among the two sets of ambiguities, the one with the smaller posterior variance was taken as the reliable ambiguity. The study showed that, for a single epoch, when only one or two satellites had incorrect ambiguities, the AR success rate after ambiguity validation and correction could reach 100% for medium baselines. For long baselines, due to the increase of atmospheric error, the validation was affected to some extent. However, the AR success rates for two long baselines increased from 96.82% and 98.44% to 98.80% and 99.67%, respectively.

The use of global navigation satellite system (GNSS) is entering a new era of joint positioning based on the use of multifrequencies and multimodes. Ensuring the correct weighting of observations from each system and satellite has become a key problem during real-time positioning. This paper addresses the issue of weights of observations as well as the quality control of GPS/BDS pseudoranges in the context of real-time relative positioning. Thus, in the first place, the Helmert variance component estimation (VCE) is used to determine the relative weighting of observations from the two systems, and then, we introduce robustness estimation theory and construct a new method. The method is resistant to the influence of outliers in the observations by selecting weight iterations. To do this, we selected GPS/BDS observation data at baseline lengths of 40 km, 46 km, and 64 km for verification and analysis. Experimental results show that, in terms of the relative positioning of medium-to-long baseline based on GPS/BDS pseudorange observations, when observed values incorporate large gross errors, our method can reduce the weighting of suspicious or abnormal values and weaken their impact on positioning solutions, so that the positioning results will not appear to have large deviation.