Abstract: A pedestrian adaptive zero-velocity update point selection method based on a neural network, including the following steps: S1, collecting inertial navigation data of different pedestrians in different motion modes; S2, preprocessing the inertial navigation data collected in the step S1, labeling the preprocessed data, and obtaining a training data set, a validation data set, and a test data set according to the preprocessed data and a label corresponding to the preprocessed data; S3, inputting the training data set to a convolutional neural network for training, obtaining a pedestrian adaptive zero-velocity update point selection model based on the convolutional neural network, and using the validation data set to validate the pedestrian adaptive zero-velocity update point selection model; and S4, inputting the test data set into the pedestrian adaptive zero-velocity update point selection model based on the convolutional neural network, and obtaining a selection result of pedestrian zero-velocity update poi

Abstract: A pedestrian adaptive zero-velocity update point selection method based on a neural network, including the following steps: S1, collecting inertial navigation data of different pedestrians in different motion modes; S2, preprocessing the inertial navigation data collected in the step S1, labeling the preprocessed data, and obtaining a training data set, a validation data set, and a test data set according to the preprocessed data and a label corresponding to the preprocessed data; S3, inputting the training data set to a convolutional neural network for training, obtaining a pedestrian adaptive zero-velocity update point selection model based on the convolutional neural network, and using the validation data set to validate the pedestrian adaptive zero-velocity update point selection model; and S4, inputting the test data set into the pedestrian adaptive zero-velocity update point selection model based on the convolutional neural network, and obtaining a selection result of pedestrian zero-velocity update poi

Abstract: A mobile device (100) carried by a user has sensors providing acceleration and orientation information. These are processed to provide trajectory for the device. The processing includes improved methods using different modules (1, 2, 3,5, 6, 7) for classifying the motion (1) and detecting a user's steps (5), tracking the orientation (3) and acceleration (2) of the device (100), estimating the length of the user's steps (6) and estimating the heading (7) of the user. The trajectories are compared to a map and corrected by the constraints provided by the map. Parameters of the methods of producing the trajectories are optimised on the basis of maximising probability of the trajectory found by the map matching given the indicated trajectory.

Abstract: A method of determining the position of a mobile device uses a model. The model is constructed by obtaining a map of a geographical area, constructing the graph using the map, wherein vertices of the graph correspond to areas of the map, and two vertices are joined by an edge when the areas corresponding to the vertices are adjacent and can be travelled between by a device, and building the one or more feature functions using the graph, wherein a feature value of the one or more feature functions indicates the extent to which the observations support the device being positioned in the areas corresponding to one or more vertices of the graph.

Abstract: A method of determining the position of a mobile device uses a model. The model is constructed by obtaining a map of a geographical area, constructing the graph using the map, wherein vertices of the graph correspond to areas of the map, and two vertices are joined by an edge when the areas corresponding to the vertices are adjacent and can be travelled between by a device, and building the one or more feature functions using the graph, wherein a feature value of the one or more feature functions indicates the extent to which the observations support the device being positioned in the areas corresponding to one or more vertices of the graph.

Abstract: A mobile device (100) caried by a user has sensors providing acceleration and orientation information. These are processed to provide trajectory for the device. The processing includes improved methods using different modules (1, 2, 3,5, 6, 7) for classifying the motion (1) and detecting a user's steps (5), tracking the orientation (3) and acceleration (2) of the device (100), estimating the length of the user's steps (6) and estimating the heading (7) of the user. The trajectories are compared to a map and corrected by the constraints provided by the map. Parameters of the methods of producing the trajectories are optimised on the basis of maximising probability of the trajectory found by the map matching given the indicated trajectory.

Abstract: A method of determining the position of a mobile device using a model. The model comprises a graph comprising a set of vertices and a set of edges between the vertices, and one or more feature functions that take as input one or more vertices of the graph and a set of observations for the device, and return a feature value. The model is constructed by obtaining a map of the geographical area, constructing the graph using the map, wherein the vertices of the graph correspond to areas of the map, and two vertices are joined by an edge when the areas corresponding to the vertices are adjacent and can be travelled between by a device, and building the one or more feature functions using the graph, wherein the feature value of the one or more feature functions indicates the extent to which the observations support the device being positioned in the areas corresponding to the one or more vertices of the graph.