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For situations such as indoor and underground parking lots in which satellite signals are obstructed, GNSS cooperative positioning can be used to achieve highprecision positioning with the assistance of cooperative nodes. Here we study the cooperative positioning of two static nodes, node 1 is placed on the roof of the building and the satellite observation is ideal, node 2 is placed on the indoor windowsill where the occlusion situation is more serious, we mainly study how to locate node 2 with the assistance of node 1. Firstly, the two cooperative nodes are located with pseudo-range single point positioning, and the positioning performance of cooperative node is analyzed, therefore the information of pseudo-range and position of node 1 is obtained. Secondly, the distance between cooperative nodes is obtained by using the baseline method with double-difference carrier phase. Finally, the cooperative location algorithms are studied. The Extended Kalman Filtering (EKF), Unscented Kalman Filtering (UKF) and Particle Filtering (PF) are used to fuse the pseudo-range, ranging information and location information respectively. Due to the mutual influences among the cooperative nodes in cooperative positioning, the EKF, UKF and PF algorithms are improved by resetting the error covariance matrix of the cooperative nodes at each update time. Experimental results show that after being improved, the influence between the cooperative nodes becomes smaller, and the positioning performance of the nodes is better than before. [ABSTRACT FROM AUTHOR]

Considering the radio-based indoor positioning system pertaining to signal degradation due to the environmental factors, and rising popularity of IP (Internet Protocol) cameras in cities, a novel fusion of inertial measurement units (IMUs) with external IP cameras to determine the positions of moving users in indoor environments is presented. This approach uses a fine-tuned Faster R-CNN (Region Convolutional Neural Network) to detect users in images captured by cameras, and acquires visual measurements including ranges and angles of users with respect to the cameras based on the proposed monocular vision relatively measuring (MVRM) method. The results are determined by integrating the positions predicted by each user’s inertial measurement unit (IMU) and visual measurements using an EKF (Extended Kalman Filter). The results experimentally show that the ranging accuracy is affected by both the detected bounding box’s by Faster R-CNN height errors and diverse measuring distances, however, the heading accuracy is solely interfered with bounding box’s horizontal biases. The indoor obstacles including stationary obstacles and a pedestrian in our tests more significantly decrease the accuracy of ranging than that of heading, and the effect of a pedestrian on the heading errors is greater than stationary obstacles on that. We implemented a positioning test for a single user and an external camera in five indoor scenarios to evaluate the performance. The robust fused IMU/MVRM solution significantly decreases the positioning errors and shows better performance in dense multipath scenarios compared with the pure MVRM solution and ultra-wideband (UWB) solution. [ABSTRACT FROM AUTHOR]