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Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation.

Parag Narkhede1, Shashi Poddar2, Rahee Walambe3

  • 1Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary

This study introduces a new cascaded complementary filter for accurate attitude estimation. The novel design eliminates the need for tuning filter parameters, offering a computationally inexpensive and effective solution for sensor fusion.

Keywords:
attitude estimationcomplementary filtergyroscopeinertial sensorsmultistage filtersensor fusion

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Area of Science:

  • Robotics and Control Systems
  • Sensor Fusion and Estimation Theory

Background:

  • Attitude estimation determines an object's orientation using sensors like gyroscopes, accelerometers, and magnetometers.
  • Sensor fusion combines data from multiple sensors for precise orientation estimates.
  • Complementary filters are common but require careful tuning of gain parameters for optimal performance.

Purpose of the Study:

  • To present a novel cascaded complementary filter architecture for attitude estimation.
  • To develop a filter that is independent of gain parameters, eliminating the need for tuning.
  • To provide a computationally inexpensive and model-free attitude estimation solution.

Main Methods:

  • A cascaded architecture combining nonlinear and linear complementary filters.
  • The nonlinear filter corrects gyroscope bias.
  • The linear filter estimates the attitude angles.

Main Results:

  • The proposed filter architecture achieves promising attitude estimation results on real-world datasets.
  • Performance is comparable or superior to existing state-of-the-art algorithms.
  • The method demonstrates independence from filter parameter tuning.

Conclusions:

  • The novel cascaded complementary filter offers an effective, parameter-independent solution for attitude estimation.
  • This approach simplifies implementation and reduces computational cost.
  • The methodology shows significant potential for various applications requiring accurate orientation tracking.