A Nonlinear Filtering Approach for the Attitude and Dynamic Body Acceleration Estimation Based on Inertial and Magnetic Sensors: Bio-Logging Application

TLDR

Bio‑logging merges animal behavior and bioengineering, and attitude data are essential for computing Dynamic Body Acceleration to assess energy expenditure. The study seeks to estimate rigid body orientation and Dynamic Body Acceleration. A quaternion‑based nonlinear filter optimized with Levenberg–Marquardt combines accelerometer, magnetometer, and gyroscope measurements, and its output is compared to an MTi‑G IMU on a domestic animal. Simulations and free‑movement experiments demonstrate the filter’s effectiveness, achieving rms errors of 0.392, 0.577, and 2.521 on roll, pitch, and yaw relative to the MTi‑G.

Abstract

This paper addresses the problem of rigid body orientation and Dynamic Body Acceleration (DBA) estimation. This work is applied in bio-logging, an interdisciplinary research area at the intersection of animal behavior and bioengineering. The proposed approach combines a quaternion-based nonlinear filter with the Levenberg Marquardt Algorithm (LMA). The algorithm has a complementary structure design that exploits measurements from a three-axis accelerometer, a three-axis magnetometer, and a three-axis gyroscope. Attitude information is necessary to calculate the animal's DBA in order to evaluate its energy expenditure. Some numerical simulations illustrate the nonlinear filter performance. Some quantitative assessments prove this efficiency such as the time constant of the filter ( ) and the rms magnitude of the quaternion error ( ). Moreover, the effectiveness of the algorithm is experimentally demonstrated. In the experiments a domestic animal is equipped with an Inertial Measurement Unit (MTi-G), which provides a truth attitude for comparison with the complementary nonlinear filter. The rms difference between the filter and MTi-G outputs in the free movement experiments is within 0.392 rms on roll, 0.577 rms on pitch, and 2.521 rms on yaw.