In the thesis, we present the structures of interacting multiple model (IMM)
algorithm-based filter design for real-time motion orientation estimation and tracking
by using inertial sensor measurements in three-dimensional space. The major
sensor such as gyroscope, though has high-sensitivity characteristics, suffers from
bias build-up and error drift over time. The complementary sensors such as accelerometer
and magnetometer, on the other hand, have low sensitivity, but do
not suffer from bias problems. By using individual inertial and magnetic sensors,
measurements of multiple modes can be interactively computed. The IMM based
designs show the advantages of weighting individual sensors in different motion
states. We propose a signal processing architecture based on the IMM algorithm.
It is composed of three parallel Kalman filters (KFs), each deals with measured
signals from accelerometer, magnetometer and gyroscope, respectively. The accelerometer
cannot effectively sense the rotation around the vertical axis; while the
magnetometer can only sense the rotation around vertical axis. Therefore, estimation
accuracy with the parallel filtering arrangement of the IMM algorithm-based
structure may be affected. A scheme using the residual signal, which is computed in
the IMM, provides the information of gyroscope-based KF to the other two filters
for feasible calculation of update weights. Related research also usually combined
the information of major and complementary sensors in estimator designs. In the
literature, existing ¡§Triad¡¨ methods with quaternion-based extended Kalman filter
(EKF), process the measurements from major and complementary sensors. To
compensate the functions, we propose to use a gyroscope-based EKF and a Triad
EKF in forming a parallel multiple model-based structure. The analysis and performance
evaluation shows advantages and disadvantages of using EKFs and KFs
in IMM-based filtering approachs. Simulation results validate the proposed estimator
design concept, and show that the scheme is capable of reducing the overall
estimation errors by flexible computation of model weights.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0802110-112517 |
| Date | 02 August 2010 |
| Creators | Gao, Jian-hau |
| Contributors | Jiann-Der Lee, Miin-Jong Hao, Kuan-Chih Wang, Chin-Der Wann |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0802110-112517 |
| Rights | not_available, Copyright information available at source archive |
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