Emerging New Trends in Hybrid Vehicle Localization Systems - Global Navigation Satellite Systems: Signal,Theory and Applications - page 290

of the trust which can be placed in the correctness of the information supplied by the total
system; integrity includes the ability of a system to provide timely and valid measurements to
users ESA (n.d.). Three key components have been proposed for integrity monitoring: 1) fault
detection, 2) fault isolation, and 3) removal of faulty measurement sources from the estimates
Hewitson et al. (2004). The European Geostationary Navigation Overlay Service (EGNOS)
and the Wide Area Augmentation System (WAAS), Hewitson (2003), are developed to form a
redundant source of information for the Global Navigation Satellite Systems (GNSS) in order
to perform integrity monitoring by providing correction information.
During the last decade, monitoring the integrity of land-vehicles' localization has attracted
attention due to the increasing demand for highly reliable accurate location data. Since roving
in dense urban environments may limit access to the signals from augmentation systems such
as EGNOS or WAAS, other means of measuring integrity have been proposed Schlingelhof
et al. (2008).
For instance, Toledo-Moreo et al. (2006) presents a localization solution based on the fusion
of GNSS and INS sensors. In this fusion process an interactive multimodel method is used.
Different covariance matrices are used as a response to change in the noise behaviour. The
proposed integrity measure is based on the covariance matrix of the EKF estimation error.
Relying on the error covariance matrix can be misleading especially when experiencing
unmodeled environment noise. In other words, it is not possible in many cases to detect,
isolate, and remove the estimation faults, let alone the unavoidable false alarms.
Also, in Jabbour et al. (2008) a binary integrity decision-maker is proposed for a
map-matching localization technique in which multihypothesis road-tracking method
combines proprioceptive sensors (odometers and gyrometers) with GPS and map
information. In this work, the integrity represents high or low confidence of the location
estimate. The candidate tracks or roads are associated with a probability that is computed
using the multihypothesis road-tracking method. If one credible road exists and the
normalized innovation is below a prespecified threshold, the technique declares high
confidence location estimate. However, the lack of granularity in the integrity measure limits
the range of the integrity-level based application that can use this method.
Integrity monitoring of map-matching localization has also been proposed and tested in
Quddus (2006). However, in this work three indicators has been monitored to achieve this
task: distance residuals, heading residuals, and an indicator related to uncertainty of the map
matched position. Due to the linguistic nature of these indicators, they have been combined
using a fuzzy inference model to produce a value between 0 to 100 to indicate the integrity of
the system. The integrity threshold has been determined experimentally to be 70, where the
type of the environment experienced during the experiment was not specified. The value of
the threshold thus can be considered specific to the environment of the experiment. Therefore,
the approach might not guarantee a robust integrity monitoring. In other words, it is possible
to come across an environment that influences the system to produce both an integrity value
above the threshold and a location estimate mismatch.
7. Performance criteria and benchmarking
From the discussion above it is clear that vehicle localization is an increasingly growing area of
research. Nevertheless, there is a number of outstanding issues that still need to be addressed.