Non-GNSS Smartphone Pedestrian Navigation Using Barometric Elevation and Digital Map-Matching

Pedestrian navigation in outdoor environments where global navigation satellite systems (GNSS) are unavailable is a challenging problem. Existing technologies that have attempted to address this problemoften require external reference signals or specialized hardware, the extra size,weight, power, and cost of which are unsuitable for many applications. This article presents a real-time, self-contained outdoor navigation application that uses only the existing sensors on a smartphone in conjunction with a preloaded digital elevation map. The core algorithm implements a particle filter, which fuses sensor data with a stochastic pedestrian motion model to predict the user’s position. The smartphone’s barometric elevation is then compared with the elevation map to constrain the position estimate. The system developed for this research was deployed on Android smartphones and tested in several terrains using a variety of elevation data sources. The results fromthese experiments showthe systemachieves positioning accuracies in the tens of meters that do not grow as a function of time

Ballistic Target Tracking with Use of Cinetheodolites

The paper addresses a problem of ballistic object tracking with the use of the cinetheodolite electro-optical tracking system. Electro-optical systems are applied for acquiring the trajectory data of missiles, satellites, and rockets used for delivery of satellites to their prevised orbits. Despite the importance of such systems and their applications, in the open literature there are no publications describing tracking algorithms processing data from cinetheodolites. The paper describes a model-based algorithm of estimation of position and parameters of target motion for such a system, developed by the authors. The model of the system, nonlinear both in its description of the target dynamics and the measurement equations, is presented in detail. The proposed algorithm of estimation is also described, and chosen simulation results are included in the paper. Furthermore, a comparison of the proposed estimation algorithm with other possible, but simpler algorithms is presented

Non-GNSS Smartphone Pedestrian Navigation Using Barometric Elevation and Digital Map-Matching

Pedestrian navigation in outdoor environments where global navigation satellite systems (GNSS) are unavailable is a challenging problem. Existing technologies that have attempted to address this problemoften require external reference signals or specialized hardware, the extra size,weight, power, and cost of which are unsuitable for many applications. This article presents a real-time, self-contained outdoor navigation application that uses only the existing sensors on a smartphone in conjunction with a preloaded digital elevation map. The core algorithm implements a particle filter, which fuses sensor data with a stochastic pedestrian motion model to predict the user’s position. The smartphone’s barometric elevation is then compared with the elevation map to constrain the position estimate. The system developed for this research was deployed on Android smartphones and tested in several terrains using a variety of elevation data sources. The results fromthese experiments showthe systemachieves positioning accuracies in the tens of meters that do not grow as a function of tim