GNSS/INS integrated systems will benefit from deep integration architecture and AI technology
Global Navigation Satellite
System (GNSS) consists
of GPS, GLONASS and
Galileo which is still under
construction by the European Union.
GPS is the most widespread GNSS
in the world and applies successfully
in so many fields such as positioning,
navigation, geodesy, mapping, timing
and so on. However, GLONASS has
not done its work well for about ten
years because of lack of funds. In
summer of 2006, Russia's GLONASS
program continued its comeback and
will have a full 24-satellite constellation
by the end of 2009. Notably, China has
a regional RDSS system using three
geostationary satellites since 2000.
INS is a self-contained positioning
and attitude device. In other words, it
meets the all-environment requirement.
The primary advantage of using
INS is that velocity and position of
the vehicle can be provided with
abundant dynamic information and
excellent short term performance.
The main shortcoming is that the INS
accuracy degrades greatly over time.
There is a strong possibility that a
GNSS/INS integrated navigation
system has superior performance
in comparison with either a standalone
GNSS or INS because of
their complementary operational
characteristics. Since 1980s,
researchers have begun to investigate
GPS/INS integrated navigation
technology and the experimental
results showed that GPS/INS integrated
systems can efficiently improve
the navigation performance. With
the development and application of
low-cost inertial measurement unit
(IMU) and GNSS receiver, GNSS/
INS technology has become one of the most popular methods of
navigation for users worldwide.
On the one hand, the low-cost IMU,
especially MEMS IMU, means low
accuracy and low performance.
It is hard to be directly usable as
sole navigation systems because of
their large random errors. On the
other hand, navigation accuracy and
integrity of GNSS will be degraded
in the presence of radio frequency
interference, hostile jamming and high
dynamical situations in the so-called
navigation war which was brought
forward formally by USA in 1997.
Aiming at these problems, researchers
have recently focused their attention
on deep integration and intelligent
integration. These two methods will
improve the robustness and precision
of the integrated system greatly.
Accordingly, researchers attach more
importance to these two methods which
are regarded as the trends in GNSS/
INS integrated navigation technology.
Trend: Deeply integrated
navigation
There are three generic functional
architectures for GNSS/INS integration,
that is, loosely, tightly and deeply (also
named ultra-tightly) integrated mode.
Traditionally, most GNSS/INS hybrid
systems have been mechanized using
loose integration or tight integration.
Loosely integrated mode is the easiest
and simplest approach because it is
based on the independence of the
GNSS and INS navigation functions.
Although it provides some tolerance
to failures of subsystem components,
loosely integrated mode can not work
when GNSS receiver doesn't track
and lock at least four satellites at the same time. Tightly integrated mode
where a GNSS receiver is not
regarded as a navigation subsystem
but as a sensor that provides pseudorange
(PR) and delta pseudo-range
(DPR) was proposed to overcome the
shortcomings of loose integration. This
kind of mode benefits from GNSS
measurement updates even if there are
less than four satellites available for a
complete GNSS navigation solution.
It also reduces the complexity of the
integrated filter due to lesser correlation
of the integration variables (PR, DPR).
However, tight integration is difficult
to meet the demands of anti-jamming
and high dynamical situations.
Designers have conceived of the deeply
integrated mode which has higher
performance than loosely integrated
and tightly integrated mode. Figure
1[1] shows GNSS/INS architectures:
loosely integrated mode, tightly
integrated mode and deeply integrated
mode. For deeply integrated mode,
the GNSS measurements I (inphase)
and Q (quadrature) from the GNSS
correlator are integrated with the INS
measurements. As shown in figure
1, one of the key techniques in the
deep integration is the integration
of INS derived Doppler feedback
to the carrier tracking loops.
The deeply integrated mode provides
the following manifold advantages:
Jamming to signal (j/
s) ratio improvement
Outputs of the deeply integrated
filter are fed back into the tracking
loops and used to control the
code and carrier replica signals
for each satellite channel[2]. A
closed-loop comes into being
and remains in lock even at
low input signal-to-noise ratios
Fig.1 GNSS/INS architectures: loosely, tightly and deeply integrated mode when aided by MEMS IMU.
In principle, the antijam of GPS
receiver is about 32dB[3]. As shown
in Figure 2, GPS receiver can't trace
the signal well when there is a 0.1W
jammer only 10km far away. Antijam
improvements in deeply integrated
mode relative to non-inertial-aided
loop are 11dB. That was evaluated
over a realistic precision guided
munition (PGM) scenario in the
presence of broadband jamming [2].
Improving system accuracy
Firstly, the accuracy of the raw
GNSS measurements is increased
due to lower tracking loop
bandwidths aided by inertial data in
deeply integrated mode. Secondly,
errors of INS, mainly gyros/
accelerometers bias and scale factor
errors, is calibrated periodically by
integrated filter outputs. Thirdly,
the integrated filter (usually kalman
filter) is an optimal fusion including
GNSS signal tracking loops and
correlators which are contained in
loosely and tightly integrated mode.
High dynamic performance
Inertial data provide the dynamic
reference trajectory for the GNSS
signal integration inside the
receiver's correlators, which results
in 'dynamic-free'[4] GPS signals
that are sent to the tracking loops
facilitating a significant reduction in
the carrier tracking loop bandwidth,
hence providing accurate carrier
and code phase measurements.
The standalone GPS receiver uses a
2nd order carrier-tracking loop with a
loop bandwidth of about 12 to 18Hz.
However, deeply integrated system also adopting a 2nd
order carrier-tracking
loop the bandwidth
can be reduced to
3Hz. That means that
deep integration can
work well in high
dynamic environment.
Good technology
can lead to perfect
productions. Hereby,
a guidance, navigation
and control flight
management unit which was housed
in a small, light weight, low power
package based on deep integration and
MEMS IMU was tested successfully
for the challenging requirements of
modern tactical applications[5, 6].
Trend: Intelligent
integrated navigation
The kalman filter is the most popular
estimation tool for GNSS/INS
integration because it is optimal in
theory. However, in fact, real system
can't satisfy all requirements of KF,
such as supposed Gauss white noise,
ideal dynamics model, and none
error linearization. Furthermore,
the more widely low cost IMU
is adopted, the more obvious the
limitations of KF become.
Nevertheless, Artificial Intelligence
(AI) is a powerful tool for solving
nonlinear problems that involve
mapping input data to output data
without any prior knowledge about
the mathematical
process involved.
All kinds of
conceptual
intelligent
navigator
combining AI
techniques were
put forward to
overcome the
demerits of KF
and improve the
accuracy and
reliability of the
integrated systems.
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