Odometer Velocity and Acceleration Estimation Based on Tracking Differentiator Filter for 3D-Reduced Inertial Sensor System

Abstract

Velocity information from the odometer is the key information in a reduced inertial sensor system (RISS), and is prone to noise corruption. In order to improve the navigation accuracy and reliability of a 3D RISS, a method based on a tracking differentiator (TD) filter was proposed to track odometer velocity and acceleration. With the TD filter, an input signal and its differential signal are estimated fast and accurately to avoid the noise amplification that is brought by the conventional differential method. The TD filter does not depend on an object model, and has less computational complexity. Moreover, the filter phase lag is decreased by the prediction process with the differential signal of the TD filter. In this study, the numerical simulation experiments indicate that the TD filter can achieve a better performance on random noises and outliers than traditional numerical differentiation. The effectiveness of the TD filter on a 3D RISS is demonstrated using a group of offline data that were obtained from an actual vehicle experiment. We conclude that the TD filter can not only quickly and correctly filter velocity and estimate acceleration from the odometer velocity for a 3D RISS, but can also improve the reliability of the 3D RISS.

Usage of V2X Applications in Road Tunnels

“…Over the years, several approaches have been developed and applied with the main goal of ensuring additional data for the estimation of an accurate position based on video processing [20], sensor data acquisition [21], 3D accelerometers [22], gyroscope and odometry [23,24], beaconing [25], and other communication methods, such as wi-fi signal strength and fingerprinting [26], etc.…”

A Low Cost Civil Vehicular Seamless Navigation Technology Based on Enhanced RISS/GPS between the Outdoors and an Underground Garage

“…From pitch error Equation (14), the pitch error δp is determined by the forward accelerometer measurement error δ f v , the odometer measured forward accelerate error δa od , and also, the Earth gravity error δg. Equation (15) shows the roll error. The roll error δr is determined by the transversal accelerometer measurement error δ f x , odometer measurement error δV od , vertical gyroscope measurement error δw z , pitch error δp and also, Earth gravity error δg.…”

“…In addition, within the constraints of the costs and environment, the algorithm of this paper is only the initial algorithm of the navigation system. In the next stage, underground garage navigation can also introduce WiFi positioning, radio frequency identification (RFID) positioning, laser scanner simultaneous localization and mapping (SLAM) positioning and other sensor positioning, which can assist MEMS inertial navigation [15]. All the new sensors will further improve the positioning accuracy of underground garage navigation.…”

Research on Algorithm of Airborne Dual-Antenna GNSS/MINS Integrated Navigation System

“…Integrated navigation system research is a hot spot in the fields of aviation and aerospace [ 1 ]. At present, the most widely used integrated navigation method in the aviation field is the integration of GNSS (global navigation satellite system) and INS (inertial navigation system) [ 2 , 3 ]. GNSS is a system capable of global positioning and time synchronization.…”