outperforms either stand-alone system operated (Yang, 2008).
There are different integrated schemes including loosely-
coupled, tightly-coupled, and ultra-tightly coupled INS/GPS
integrated strategies, have been researched and developed since
the last decade (Petovello, 2003).
The sustainability of INS/GPS integrated system using current
commercially available micro-electro-mechanical systems
(MEMS) inertial technology in typical GPS denied
environments is fragile. However, the progress of MEMS
inertial sensors is advanced rapidly thus the inclusion for
general land vehicular navigation is bright in the future. In
addition to waiting for the advanced development process of
MEMS inertial sensor, some measures have been taken to
increase the sustainability of MEMS INS/GPS integrated
systems for vehicular applications during frequent signal
blockages in software aspect (Chiang et al, 2003; Chiang and
Huang, 2008). In other words, aiding the INS with other
complementary sensors is critical to improve the accuracy of
inertial based navigation systems. Choosing an appropriate
estimation method is a key issue in developing an aided INS
(Shin, 2005).
2. PROBLEM STATEMENTS
It is common practice to use Extended Kalman Filter (EKF) to
accomplish the data fusion. Several architectures for EKF
implementations are known (Wendel and Trommer, 2004). The
most common integration scheme used today is loosely-coupled
(LC) integration scheme. It is the simplest way of integrating a
GPS processing engine into an integrated navigation system.
The GPS processing engine calculates position fixes and
velocities in the local level frame and then send the solutions as
measurement update to the main INS EKF. By comparing the
navigation solutions provided by INS mechanization with those
solutions provided by GPS processing engine, those navigation
states can be optimally estimated, as shown in Figure 2, the
primary advantage of LC architecture is the simplicity of its
implementation, because no advanced knowledge of GPS
processing is necessary. The disadvantage of implementation is
that the measurement update of the integrated navigation system
is only possible when four or more satellites are in view.
g . Loosely-coupled INS/GPS integration architecture
On the other hand, the tightly-coupled (TC) integration scheme
uses a single KF to integrate GPS and IMU measurements. In
the TC integration, the GPS pseudo-range and delta-range
measurements are processed directly in the main KF, as shown
in Figure 3. For some references, the aiding of the receiver
tracking loops using velocity information provided by the INS
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
is an essential characteristic of tightly-coupled scheme, too. The
primary advantage of this integration is that raw GPS
measurements can still be used to update the INS when less
than four satellites are available. This is of special benefit in a
hostile environment such as downtown areas where the
reception of the satellite signals is difficult due to obstruction
when the vehicle navigates in urban or suburban area.
Figure 3. Tightly-coupled INS/GPS integration architecture
However, according to Chiang and Huang (2008), the EKF
implemented with a TC scheme may come with serious
problems concerning the quality of GPS raw measurements. In
other words, EKF based TC architecture is sensitive the quality
of GPS raw measurements. This scenario usually takes place in
urban and suburban areas because of the impact of reflected
GPS measurements. Therefore, this study applied the Adaptive
Kalman Filter (AKF) as the core estimator of a tightly-coupled
INS/GPS integrated scheme by tuning the measurement noise
matrix R adaptively. The idea of AKF is based on the maximum
likelihood criterion for choosing the most appropriate weight
and thus the Kalman gain factors. The conventional EKF
implementation suffers uncertain results while the update
measurement noise matrix R and/or the process noise matrix Q
does not meet the case.
3. THE IMPLEMENTAION OF AKF SCHEMES
The AKF can be implemented by Multi model adaptive
estimation (MMAE) and Innovation-based adaptive estimation
(IAE), respectively. Those methods need to calculate the
innovation sequence, which is obtained by the difference
between real measurement received by the filter and predicted
value. At the current epoch &, not only the new measurement
but the predicted value provides the new information. Hence,
the innovation sequence represents the information satisfy the
new measurement and considered as the most relevant source of
the adaptive filter. See Genin (1970), Kailath (1972) and
Kailath (1981) for more details. The primary advantage of AKF
is that the filter has less relationship with the priori statistical
information because the R and/or Q matrices vary with time.
According to Schwarz and Mohamed (1999), the IAE scheme is
more efficient than MMAE scheme. Therefore, the IAE scheme
is chosen in this study. The innovation sequence is used to
derive the measurement weights through the covariance matrix
R in this study. In the IAE method implemented in study, the
covariance matrix R is adapted when measurements update with
time. A window based approach is implemented to update the
quality of GPS pseudo-range measurements by adaptively
