Bridging GPS outages of tightly-coupled GPS/SINS using GMDH neural network

A tightly coupled GPS （ global positioning system ）/SINS （ strap down inertial navigation system） based on a GMDH （ group method of data handling） neural network was presented to solve the problem of degraded accuracy for less than four visible GPS satellites with poor signal quality. Positions and velocities of the satellites were predicted by a GMDH neural network, and the pseudo ranges and pseudo range rates received by the GPS receiver were simulated to ensure the regular op eration of the GPS/SINS Kalman filter during outages. In the mathematical simulation a tightly cou pled navigation system with a proposed approach has better navigation accuracy during GPS outages, and the anti jamming ability is strengthened for the tightly coupled navigation system.

Indoor positioning based on tightly coupling of PDR and one single Wi-Fi FTM AP

The indoor positioning system is now an important technique as part of the Internet-of-Things(IoT)ecosystem.Among indoor positioning techniques,multiple Wi-Fi Access Points(APs)-based positioning systems have been researched a lot.There is a lack of research focusing on the scene where only one Wi-Fi AP is available.This work proposes a hybrid indoor positioning system that takes advantage of the Fine-Timing Measurements(FTM)technique that is part of the IEEE 802.11mc standard,introduced back in 2016.The system uses one single Wi-Fi FTM AP and takes advantage of the built-in inertial sensors of the smartphone to estimate the device’s position.We explore both Loosely Coupled(LC)and Tightly Coupled(TC)integration schemes for the sensors’data fusion.Experimental results show that the proposed methods can achieve an average positioning accuracy of about 1 m without knowing the initial position.Compared with the LC integration method,the median error accuracy of the proposed TC fusion algorithm has improved by more than 52%and 67%,respectively,in the two experiments we set up.

A Robust Graph Optimization Realization of Tightly Coupled GNSS/INS Integrated Navigation System for Urban Vehicles

This paper describes a robust integrated positioning method to provide ground vehicles in urban environments with accurate and reliable localization results. The localization problem is formulated as a maximum a posteriori probability estimation and solved using graph optimization instead of Bayesian filter. Graph optimization exploits the inherent sparsity of the observation process to satisfy the real-time requirement and only updates the incremental portion of the variables with each new incoming measurement. Unlike the Extended Kalman Filter （EKF） in a typical tightly coupled Global Navigation Satellite System/Inertial Navigation System （GNSS/INS） integrated system, optimization iterates the solution for the entire trajectory. Thus, previous INS measurements may provide redundant motion constraints for satellite fault detection. With the help of data redundancy, we add a new variable that presents reliability of GNSS measurement to the original state vector for adjusting the weight of corresponding pseudorange residual and exclude faulty measurements. The proposed method is demonstrated on datasets with artificial noise, simulating a moving vehicle equipped with GNSS receiver and inertial measurement unit. Compared with the solutions obtained by the EKF with innovation filtering, the new reliability factor can indicate the satellite faults effectively and provide successful positioning despite contaminated observations.