|
| The implementation of an Extended Particle Filter (EPF) was proposed as an estimation
technique for integrated GPS and low-cost inertial MEMS navigation systems |
|
| Conclusions |
In this paper, the implementation of
an Extended Particle Filter (EPF) was
proposed as an estimation technique for
integrated GPS and low-cost inertial
MEMS navigation systems. The performance of the proposed algorithm
was compared to the corresponding
performance of the commonly used
Extended Kalman Filter (EKF) using field test INS/GPS land-vehicle data set. |
|
 |
The results showed that both of the
EPF and the EKF provided comparable
navigation errors in cases of full GPS
signals availability. However in cases
when short period (60 sec) GPS outages
were simulated in the test trajectory,
EPF performance is slightly better
than that of EKF. It was also deducted
that the performance of EPF is highly
dependent on the number of particles
used in the implementation. Generally
the number of particles required
varies within 15 to 100 particles for
each state variable. For Motion Pak
II, 35 particles gave best results. |
Generally, in cases when non-linearities
are dominant, the EKF performance is
known to be greatly degraded. However,
this is not the case for the EPF since the
EPF principle is based on the fact that
a single state vector is represented by
number of particles and therefore, it is
more reliable for highly nonlinear cases.
To further improve the performance of
EPF, further investigations are required |
 |
including the tuning of the
process noise, measurement
noise and the importance
sampling step. These techniques
are currently being investigated. |
| Acknowledgements |
This study was supported in
part by research funds from the
Natural Science and Engineering
Research Council of Canada
(NSERC) and the Canadian
Geomatics for Informed
Decisions (GEOIDE) Network |
Centers of Excellence (NCE) to Dr. Naser
El-Sheimy. The authors would like to
thank Dr. Xiaoji Niu, Dr. Kaiwei Chiang,
Mr. Chris Goodall and Zainab Syed for
their help in collecting the field data sets. |
| References: |
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