The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
860 
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).