Indoor navigation and location-based services increasingly show promising marketing prospects. Indoor positioning based on Wi-Fi radio signal has been studied for more than a decade because Wi-Fi, a signal of opportunity without extra cost, is extensively deployed for internet connections. Bayesian fingerprinting positioning, a classical Wi-Fi-based indoor positioning method, consists of two phases: radio map learning and position inference. Thus far, the application of Bayesian fingerprinting positioning is limited due to its poor usability; radio map learning requires an adequate number of received signal strength indication (RSSI) observables at each reference point, long-term fieldwork, and high development and maintenance costs. In this paper, based on a statistical analysis of actual RSSI observables, a Weibull–Bayesian density model is proposed to represent the probability density of Wi-Fi RSSI observables. The Weibull model, which is parameterized with three parameters that can be calculated with fewer samples, can calculate the probability density with a higher accuracy than the traditional histogram method. Furthermore, the parameterized Weibull model can simplify the radio map by storing only three parameters that can restore the whole probability density, i.e., it is not necessary to store the probability distribution based on traditionally separated RSSI bins. Bayesian positioning inference is performed in the positioning phase using probability density rather than the traditional probability distribution of predefined RSSI bins. The proposed method was implemented on an Android smartphone, and the performance was evaluated in different indoor environments. Results revealed that the proposed method enhanced the usability of Wi-Fi Bayesian fingerprinting positioning by requiring fewer RSSI observables and improved the positioning accuracy by 19–32% in different building environments compared with the classic histogram-based method, even when more samples were used.

Indoor navigation and positioning services for pedestrians are challenging because of the lack of satellite signals and the unpredictability of pedestrian motion. The inertial measurement unit (IMU)-based pedestrian dead reckoning (PDR) algorithm can provide continuous position estimation for individual pedestrians. However, the accumulation of errors leads to inaccurate pedestrian position results. Radio signals such as ultra-wideband (UWB) can range between pedestrians and anchors and provide high-precision positioning information; nonetheless, radio positioning requires infrastructure deployment and maintenance in indoor environments, thus limiting the popularization and implementation of these technologies. In this paper, a neural network aided factor graph optimization (NN-FGO) method was proposed for collaborative pedestrian navigation (CPN). It integrates IMU and UWB sensors to implement PDR for individual pedestrians and CPN for the Ad-Hoc network, and it is infrastructure-free since all the sensors are wearable. For a small or sparse network, ranging constraints will be insufficient to implement an acceptable CPN. A neural network model was suggested for human activity recognition and position loopback detection, which provide virtual constraints for pedestrians. For the heterogeneous problem caused by multiple collaborative signals and constraints, FGO was employed to solve the motion states of multi-pedestrians and multi-epochs. The real experimental results revealed that NN-FGO can provide 92% accuracy in activity classification. Compared with the extended Kalman filter based CPN, the average position error decreased by 19.6% and 16.0% with triangular and parallel straight geometries, respectively.

In this paper, we use multimode GNSS instead of single GPS constellation to resolve the three exterior line elements of each image. The principles of differential GNSS positioning and GNSS-supported aerial triangulation (AT) are presented and an implementation case is demonstrated. With multi-constellation system, the number of visible satellites is significantly increased and the geometry distribution of the satellites is well improved. The positioning accuracy and robustness are therefore getting better compared to GPS positioning. Experimental results show that differential GNSS has remarkable increment on the integer rate of ambiguity solution when GPS has few number and low elevation angle of satellites. The combined AT adjustment of GNSS resolution and 10 ground control points (GCPs) achieve the horizontal accuracy of ±18 cm and vertical accuracy of ±23 cm for the check points, which are comparable with traditional bundle adjustment with dense GCPs and better than GPS-supported AT. The achieved accuracy also satisfies the requirement for 1:500 topographic maps with the bonus of 84% GCPs eliminated. In conclusion, GNSS-supported AT is of feasibility and superiority for large scale Unmanned Aerial Vehicle-based photogrammetry.

The thermal properties of refrigerants can be modified by adding porous nanoparticles into them. Here, molecular simulations, including molecular dynamics and grand canonical Monte Carlo, were employed to study the thermal energy storage properties of an R161/MOF-5 nanofluid. The results show that the thermodynamic energy change of MOF-5 nanoparticles is linear to the temperature. The adsorption heat calculated by grand canonical Monte Carlo is close to that calculated by the Clausius–Clapeyron equation. Additionally, a negative enhancement of the thermal energy storage capacity of the R161/MOF-5 nanofluid is found near the phase transition area.

Cycle slip (CS) is a primary error source in Precise Point Positioning/Inertial Navigation System (PPP/INS) integrated systems. In this study, an INS-aided CS detection and repair method is presented. It utilizes high-precision INS information instead of a pseudorange to remove the satellite–receiver geometric range in the wide-lane (WL) and ionospheric-free (IF) phase combinations and creates an INS-aided WL (WL-INS) model and an INS-aided IF (IF-INS) model. Since INS information is superior to pseudorange, the INS-aided models have high detection accuracy. However, the effectiveness of INS-aided models cannot persist for a long time because of INS accumulation error. To overcome the disturbance of INS error, improved INS-aided models are proposed. This idea takes advantage of the long wavelength of WL combination and tries to fix WL CS. Once it succeeds, the INS error can be evaluated and removed. The proposed method was tested using land vehicle data, in which simulated cycle slips and signal interruption were introduced. The results show that this method can accurately detect and repair different cycle slips between the continuous Global Positioning System (GPS) epoch. When it comes to the cycle slip after a GPS interruption, the method can also accelerate PPP re-convergence, as it is not affected by the inertial accumulation error.