In this paper, an extended Kalman filter-based estimator adopting a dynamic vehicle model for determining the vehicle’s longitudinal and lateral velocity as well as the yaw rate is proposed. Two additional adaptation states are introduced to scale longitudinal and lateral tire forces if necessary to account for uncertainties in the tire/road contact. As excitation plays a vital role as far as observability is concerned, the suggested approach assesses local observability online and keeps an unobservable adaptation state constant by introducing the respective state as a virtual measurement variable when losing local observability. Furthermore, the filter is part of a Global Navigation Satellite System (GNSS)-based estimation framework. It exploits the availability of a GNSS-based horizontal velocity estimate instead of wheel speeds as aiding measurement, thus being independent of wheel slip. Experimental results for scenarios with different kinds of excitation show the effectiveness of the proposed estimator in the nominal as well as in the perturbed vehicle parameter case requiring filter adaptation.

Adaptive EKF-Based Vehicle State Estimation With Online Assessment of Local Observability

Nonlinear observer design for GNSS-aided inertial navigation systems with time-delayed GNSS measurements

A nonlinear observer structure for estimating position, velocity and attitude in a GNSS/INS system with delayed GNSS-measurements is proposed. The structure consists of an inertial measurement data buffer, a nonlinear state observer and a fast simulator. The inertial and magnetometer measurements are synchronized and delayed to coincide in time with the GNSS-measurements. The state observer will hence estimate the delayed states, and a fast simulator is employed to recover the current states using inertial measurements. The nonlinear observer is semi-global exponentially stable. The magnitude and effect of the GNSS delay is investigated, and implementation aspect of the nonlinear observer and the fast simulator are discussed. The proposed observer structure is verified by comparison to an observer without time-synchronization through use of simulations and experimental data from a UAV flight test.

/pdf/nonlinear-observer-for-ins-aided-by-time-delayed-gnss-43ryihhe7c.pdf

Nonlinear observer for INS aided by time-delayed GNSS measurements: Implementation and UAV experiments

Cloud computing supports many unprecedented cloud-based vehicular applications. To improve connectivity and bandwidth through programmable networking architectures, Software- Defined (SD) Vehicular Network (SDVN) is introduced. SDVN architecture enables vehicles to be equipped with SDN OpenFlow switch on which the routing rules are updated from a SDN OpenFlow controller. From SDVN, new vehicular architectures are introduced, for instance SD Vehicular Cloud (SDVC). In SDVC, vehicles are SDN devices that host virtualization technology for enabling deployment of cloud-based vehicular applications. In addition, the migration of Virtual Machines (VM) over SDVC challenges the performance of cloud-based vehicular applications due the highly mobility of vehicles. However, the current literature that discusses VM migration in SDVC is very limited. In this paper, we first analyze the evolution of computation and networking technologies of SDVC with a focus on its architecture within the cloud-based vehicular environment. Then, we discuss the potential cloud-based vehicular applications assisted by the SDVC along with its ability to manage several VM migration scenarios. Lastly, we provide a detailed comparison of existing frameworks in SDVC that integrate the VM migration approach and different emulators or simulators network used to evaluate VM frameworks’ use cases.

https://www.mdpi.com/1424-8220/20/4/1092/pdf

Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration

/pdf/optimal-vehicle-dynamics-control-and-state-estimation-for-a-1rhbggnb24.pdf

Optimal vehicle dynamics control and state estimation for a low cost GNSS-based collision avoidance system

Unmanned Aerial Vehicle (UAV) technology is now commonplace in many defence and civilian environments. However, the high cost of owning and operating a sophisticated UAV has slowed their adoption in many commercial markets. Universities and research groups are actively experimenting with UAVs to further develop the technology, particularly for automated flying operations. The two main UAV platforms used are fixed-wing and helicopter. Helicopter-based UAVs offer many attractive features over fixed-wing UAVs, including vertical take-off, the ability to loiter, and highly dynamic flight. However the control and navigation of helicopters are significantly more demanding than those of fixed-wing UAVs and as such require a high bandwidth real-time Position, Velocity, Attitude (PVA) navigation system. In practical Real-Time Navigation Systems (RTNS) there are delays in the processing of the GNSS data prior to the fusion of the GNSS data with the INS measurements. This latency must be compensated for otherwise it degrades the solution of the navigation filter. This paper investigates the effect of latency in the arrival time of the GNSS data in a RTNS. Several test drives and flights were conducted with a low-cost RTNS, and compared with a high quality GNSS/INS solution. A technique for the real-time, automated and accurate estimation of the GNSS latency in low-cost systems was developed and tested. The latency estimates were then verified through cross-correlation with the time-stamped measurements from the reference system. A delayed measurement Extended Kalman Filter was then used to allow for the real-time fusing of the delayed measurements, and then a final system developed for on-the-fly measurement and compensation of GNSS latency in a RTNS.

/pdf/latency-determination-and-compensation-in-real-time-gnss-ins-33toho464j.pdf

P. D. Solomon

Papers

Latency Determination and Compensation in Real-Time Gnss/ins Integrated Navigation Systems