The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008
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Other optical tracking systems that can be potentially used for
personal tracking make use of light to measure angles (ray
direction) that are used to find the position location (however, to
the best of the authors’ understanding, no system has been
reported so far to use this techniques for personal navigation).
The essential parts of an optical system are the target (mobile
user) and the detector (sensor). These systems rely on a clear
line-of-sight (LOS) between the detector and the target.
Detectors can be in the form of Charged Coupled Device
(CCD)-based cameras, video cameras, infrared cameras, etc.
Targets can be active, such as light-emitting diode or infrared-
emitting diode, or passive, such as mirrors or other reflective
materials, or simply natural objects (Allen et al., 2001).
Detectors are used to observe targets and to derive position and
orientation of a target from multiple angular observations
(multiple detectors). It is necessary to mention here another type
of optical tracking systems, based on laser ranging, which
provides range measurements to active or passive targets. This
method is well suited for measuring distances from several
meters to a few hundreds of meters, and even considerably
longer distances, and thus, it is suitable for both outdoor and
indoor applications. The accuracy of the distance measured
ranges from micrometers for short-range devices, to a
decimeter-level for very long-range systems (see, e.g., Soloviev
et al., 2007, for urban navigation application of this technique).
1.2 Navigation of Pedestrians vs. Military and Emergency
Personnel Navigation
Over the past decade, due to the widespread use of GPS, the US
military has become increasingly dependent on precision
navigation and timing (PNT). Military strategy and tactics have
evolved to assume the availability and integrity of accurate
position, navigation and timing information based on GPS. In
fact, one of the key enablers of precision and net-centric warfare
is high-accuracy PNT, currently predominantly provided by
GPS. One of the crucial applications of PNT is accurate and
reliable navigation and tracking of ground personnel in combat
and emergency situations. Protecting ground troops or
emergency/disaster management crews, while maintaining the
effectiveness of the combat or rescue operation, requires precise
individual geolocation of all military and emergency personnel
in real-time.
However, GPS is not effective in electromagnetically and
physically impeded environments. There are also environments
where GPS is significantly degraded or not available.
Unfortunately, with the global war on terror, the military
operations have become more focused on these types of
environments. Thus, there is an urgent need to develop
autonomous robust navigation theories and algorithms that
provide assured GPS-level performance in all environments,
thereby extending the reach of precision combat into these hard-
to-navigate, high-importance areas. While the integration of
Inertial Navigation System (INS) data with GPS data is a
common navigation solution in use today, the PNT performance
of GPS/INS systems can degrade rapidly when GPS is not
available. The development of lower-cost, high-accuracy
imaging and ranging devices, e.g., digital cameras, scanning
Light Detection And Ranging (LiDAR), flash-LADAR (LAser
Detection And Ranging), mm-wave RADAR, and more, have
shown promise in providing information which can be used to
aid a GPS/INS system in urban environments where GPS
signals may be blocked by topography or denied by interference.
Currently, a significant body of research is underway to address
the problem of assured navigation in all environments. However,
it is a difficult and complex problem, which requires a
multidisciplinary approach to address the fundamental
challenges that must be overcome to realize a truly autonomous
assured navigation and timing capability. This paper only
touches one aspect of this complex problem - personal
multisensor navigation, where in addition to a number sensors
listed in Table 1, human body is also considered as a sensor, and
its dynamic modeling is used to support dead reckoning
navigation mode in situations where all other sensors may fail.
2. PERSONAL NAVIGATOR BASED ON HUMAN
LOCOMOTION MODEL
2.1 Human Body as Navigation Sensor
Recent years brought many new developments in computational
intelligence (Cl) techniques leading to an exponential increase
in the number of applications in numerous areas, such as
engineering, social and biomedical. In particular, Cl techniques
are very suitable in applications related to human motion
modeling, and are being increasingly used for this purpose, due
mainly to the complexity of the biological systems as well as
the limitations of the existing quantitative techniques in
modeling. Examples of algorithms and methods used in Cl are
Artificial Neural Networks (ANNs) and Fuzzy Logic (FL).
Using Cl methods allows for better process control and more
reliable prediction/modeling of the processes under
consideration. In our case, the ANN (e.g., Kaygisiz et al., 2003;
Chiang et al., 2003; Wang et al., 2006; Grejner-Brzezinska et
al., 2006c and 2007a and b; Moafipor et al., 2007) and FL (e.g.,
Sasiadek and Khe, 2001; Kosko, 1991) are used to model a
simplified human dynamics model that consists of step length
(SL) and step frequency (SF), which together with the direction
of motion (step direction, SD) are used to navigate the mobile
operator in the dead reckoning mode. The human dynamics
model is calibrated while other sensors, primarily GPS, provide
continuous navigation solution, and the human-based sensors
are used in situation where other sensors cease to operate
(Grejner-Brzezinska et al., 2006a-c; 2007a and b; Moafipoor
2007a and b; Toth et al., 2007).
In the current concept design, the prototype of a personal
navigator is based on multi-sensor integration in a backpack
configuration, augmented by the human locomotion model that
supports navigation during GPS gaps. The navigation accuracy
requirement is at 3-5 m CEP (circular error probable) 50% level.
At the current stage of the research, the algorithmic concept of
the GPS-based, IMU-augmented personal navigator system with
an open-ended architecture has been implemented (see Figure 1).