In this paper, we propose a new method for a land-vehicle
In-Motion Alignment by integrating Inertial Navigation Sys-
tem, the transfer (code-based) Differential GPS and Vehicle
Motion Sensor (INS/DGPS/VMS). The VMS can provide
moderately accurate speed of vehicle from measured wheel
revolution by an optical encoder. It is similar to the so-called
odometer. Our aim in this work is to integrate the advantages
of these systems and to develop the navigation system that
does not require initial attitudes information of the vehicle°
In case of our previous conventional navigation system, the
initialization of INS navigation states is completed prior to
vehicle motion. By stopping the vehicle at the start point,
these initial states can be computed by integrating INS data
with static navigation data such as velocity 0 (ft/s) and ini-
tial position obtained from DGPS. However this initializa-
tion method usually requires 5 - 10 minutes and thus the
vehicle must be stopped. However it is common occurrence
that there is not enough time to stop at the start point. Thus,
for reducing the initial alignment time, developing the In-
Motion Alignment algorithm is desired.
In case of In-Motion Alignment, there exist some similar
works such as INS/GPS integrated system. In these works,
it is assumed that the external information, i.e. GPS navi-
gation data can be obtained continuously. In other words,
if the GPS signal is not available because of obstructions
such as tall buildings in the city, they cannot be performed.
Therefore we propose a new integrated INS/DGPS/VMS In-
Motion Alignment by applying the Kalman filter. Substi-
tuting the VMS for DGPS as DGPS is not utilized, the al-
gorithm is able to continue In-Motion Alignment in all cir-
cumstances.
In this paper, we consider In-Motion Alignment by using
DGPS and VMS properly. When the DGPS is available,
accurate velocity and position data can be obtained and
they are used as the measurement to estimate the INS er-
rors by the Kalman filter (INS/DGPS mode). On the other
hand, VMS sensor provides only speed data. Therefore if
the DGPS signals are not available, VMS speed is used as
the measurement corresponding to DGPS (INS/VMS mode).
For INS error model of the algorithm, the large azimuth er-
ror model which formulates INS error (position, velocity,
attitude) equations for large initial heading error and sensor
error [ 1] is adopted with some suitable modifications for our
coordinate system•
The experimental results show that, in case of In-motion
alignment, although GPS navigation data are utilized dis-
continuously, our INS/DGPS/VMS integration switching
mode provides almost the same accurate navigation data
(position, velocity, attitude) comparing with INS/DGPS
mode under GPS navigation data obtained continuously.