Validation and Performance Evaluation of a Simulink Inertial Navigation System Simulator

Collaborative Navigation Simulation Tool Using Kalman Filter With Implicit Constraints

“…1. Starting from velocity and attitude time series (Giroux et al (2003) or Jwo et al (2014)) 2. Starting from waypoints (coordinate time series) that are interpolated using cubic spline in order to achieve a smooth trajectory and velocity information, (Garcia-Fernandez and Schön (2017)).…”

“…Starting from waypoints (coordinate time series) that are interpolated using cubic spline in order to achieve a smooth trajectory and velocity information, (Garcia-Fernandez and Schön (2017)). The remaining kinematic variables can be obtained following the approach of Giroux et al (2003).…”

“…Given that the tool provides different possibilities to simulate the vehicle dynamics and environmental characteristics, it is possible to analyze the performance of different sensor fusion strategies in any desired situation. This improves simulation approaches such as those implemented by Giroux et al (2003), Jwo et al (2014) or Gade (2005) -which only consider the vehicle dynamics' simulation-, or filter approaches such as Vogel et al (2018), where only one vehicle is considered. In addition, the collaborative navigation approach using GNSS baselines to link the vehicles allows us to estimate the state parameters from each vehicle simultaneously in the same coordinate frame (unlike the approach presented by de Ponte-Müller et al (2016), where the relative position and velocity of the observed vehicles are estimated in the ego-vehicle navigation frame).…”

“…In the case of collaborative scenarios, the development of a simulation tool helps to cost-effectively and safely reproduce a wide spectrum of realistic situations in order to evaluate the performance of different sensor setups and configurations under different environmental scenarios. Simulation environments as the ones designed by Giroux et al (2003), Jwo et al (2014) or Gade (2005) represent valid simulation and post-processing methodologies for positioning and inertial measurements, although no environmental characteristics are considered. Zhang et al (2018) use 3D city models in order to overcome the GNSS limitations in urban canyons using a collaborative approach.…”

Inertial Navigation System/Global Positioning System Fusion Algorithm Design in a Fast Prototyping Environment: Towards a Real-Time Implementation

“…It has been already shown that Simulink is an interesting tool for the integration of the equations of motion (Giroux et al, 2003). However, the implementation of an extended Kalman filter (EKF) for INS/GPS fusion in a graphical-coding environment is not a trivial task.…”