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

Qing Zhang1, Lianwu Guan2, Dexin Xu3

  • 1College of Automation, Harbin Engineering University, Harbin 150001, China. zhq402@hrbeu.edu.cn.

Sensors (Basel, Switzerland)
|October 20, 2019
PubMed
Summary
This summary is machine-generated.

A novel tracking differentiator (TD) filter enhances 3D reduced inertial sensor systems (RISS) by accurately estimating velocity and acceleration from noisy odometer data. This method improves navigation accuracy and reliability without complex object models.

Keywords:
land vehicles navigationphase lag compensationreduced inertial sensor systemtracking differentiator filtervelocity estimation

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Area of Science:

  • Robotics
  • Sensor Fusion
  • Navigation Systems

Background:

  • Reduced Inertial Sensor Systems (RISS) rely heavily on odometer velocity, which is susceptible to noise.
  • Conventional differentiation methods amplify noise, compromising navigation accuracy and reliability in 3D RISS.
  • Accurate velocity and acceleration estimation are crucial for robust RISS performance.

Purpose of the Study:

  • To propose and evaluate a tracking differentiator (TD) filter for improved velocity and acceleration tracking in 3D RISS.
  • To enhance navigation accuracy and reliability by mitigating noise in odometer velocity data.
  • To demonstrate the TD filter's effectiveness compared to traditional numerical differentiation.

Main Methods:

  • Implementation of a tracking differentiator (TD) filter to estimate velocity and acceleration from odometer signals.
  • Utilizing the TD filter's prediction process to reduce phase lag.
  • Conducting numerical simulations and analyzing offline data from actual vehicle experiments.

Main Results:

  • The TD filter accurately estimates velocity and acceleration, filtering out random noise and outliers.
  • Numerical simulations show superior performance of the TD filter over traditional differentiation methods.
  • Vehicle experiment data confirm the TD filter's effectiveness in a real-world 3D RISS context.

Conclusions:

  • The TD filter effectively filters odometer velocity and estimates acceleration for 3D RISS.
  • The proposed method significantly improves the accuracy and reliability of 3D RISS navigation.
  • The TD filter offers a computationally efficient solution without requiring object models.