GPS/BDS/INS tightly coupled integration accuracy improvement using an improved adaptive interacting multiple model with classified measurement update

An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the performance.In this research, an Adaptive Interacting Multiple Model（AIMM） filter is developed to enhance performance. The soft-switching property of Interacting Multiple Model（IMM） algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System（GPS） and Bei Dou Navigation Satellite System（BDS） were post-processed in differential mode.The results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpartfor GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.

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.

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.

GIL:a tightly coupled GNSS PPP/INS/LiDAR method for precise vehicle navigation

Accurate positioning and navigation play a vital role in vehicle-related applications,such as autonomous driving and precision agriculture.With the rapid development of Global Navigation Satellite Systems(GNSS),Precise Point Positioning(PPP)technique,as a global positioning solution,has been widely applied due to its convenient operation.Nevertheless,the performance of PPP is severely affected by signal interference,especially in GNSS-challenged environments.Inertial Navigation System(INS)aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments,but suffers from degradation during GNSS outages.LiDAR(Laser Imaging,Detection,and Ranging)-Inertial Odometry(LIO),which has performed well in local navigation,can restrain the divergence of Inertial Measurement Units(IMU).However,in long-range navigation,error accumulation is inevitable if no external aids are applied.To improve vehicle navigation performance,we proposed a tightly coupled GNSS PPP/INS/LiDAR(GIL)integration method,which tightly integrates the raw measurements from multi-GNSS PPP,Micro-Electro-Mechanical System(MEMS)-IMU,and LiDAR to achieve high-accuracy and reliable navigation in urban environments.Several experiments were conducted to evaluate this method.The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors(RMSEs)of the proposed GIL method have the improvements of 63.0%,51.3%,and 62.2%in east,north,and vertical components,respectively.The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment(i.e.,the environment with GNSS signals partly-blocked)and meter-level positioning accuracy in GNSS difficult environment(i.e.,the environment with GNSS hardly used).Besides,the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.

Low Cost GPS/INS Carrier Phase Integrated Navigation System

Integrated GPS/INS systems have applications in many different fields. It has been recognized that tightly coupled GPS/INS performance is superior to loosely coupled. The tightly coupled system uses raw data of GPS as measurements, such as pseudorange, carrier phase. This paper presents two antenna GPS carrier phase measurements to stabilize the azimuth of integrated system. The test results for the integrated system show an attitude accuracy of up to 0.1° (RMS) for pitch and roll, and up to 0.03°(RMS) for azimuth when baseline is 7 8m. The system by this integration can provide the position and velocity of high accuracy too.

Design and test of HiSGR: a novel GNSS/INS ultra tight coupled receiver

With the support of the national nature science foundation,the Academy of Space Electronic Information Technology is developing a novel compact spaceborne GNSS receiver,referred to as the HiSGR(High Sensitivity GNSS Receiver).This receiver can operate effectively in the full range of Earth orbiting missions,from LEO(Low Earth Orbit)to geostationary and beyond.Improved signal detection algorithms are used in the signal process section of the HiSGR and an inertial sensor is used for GNSS/INS ultra tight coupled design,which makes the acquisition process fast and provides improved tracking performance for weaker GPS signals in the presence of high dynamics.Extensive tests are performed using the HiSGR to demonstrate the good performance of some crucial specifications,by employing a real GNSS signal received in an open field and through hardware-in-the-loop simulation.Receiver performance is demonstrated for LEO and GEO scenarios.A ground vehicle running test is performed for demonstration of fast acquisition and reacquisition capabilities under conditions of signal loss.The HiSGR showed good performance and it was stable during the simulations and tests,which proved its capability for future space applications.

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.