59 unmanned systems
inside
April/May 2016
ENGINEERING. PRACTICE. POLICY.
by a reconfigurable integration filtering engine
(RIFE). The navigation filter mechanization is
abstracted into object-oriented multi-sensor
estimation. Various sensors are represented by
generic classes in the RIFE library. Each class
is designed for a generic type of sensors (rather
than for a specific sensor) wherein sensor types
are defined by the type of measurement. When a
sensor is connected to the system, the RIFE is re-
configured by identifying the sensor's measure-
ment type and activating a sensor object using a
corresponding class from the RIFE class library
without the need of redesign or any new coding.
This paper introduces a concept of plug and
play navigation and demonstrates its perfor-
mance for precise positioning in GNSS-chal-
lenged environments. The remainder of the pa-
per is organized as follows. Section II describes
the architectural mechanization of RIFE. Sec-
tion III introduces a specific use case where the
application of RIFE is particularly beneficial
from the perspectives of navigation accuracy
and system re-configurability. As such we con-
sider connected cars for automotive safety where
lane-level accurate positioning is required. Sec-
tion IV shows experimental results for various
sensor configurations and representative test
scenarios in downtown San Francisco, CA. Test
results demonstrate that RIFE enables lane-lev-
el positioning accuracy in dense urban canyons
while using consumer-grade sensors suitable for
automotive applications.
Figure 1: UAV mission example
Figure 2: Existing state-of-the-art: Sensor-specif c solutions that have
to be redesigned when a sensor conf guration is changed
Figure 3: Plug and play solution: Generic sensor fusion automatically
reconf gures itself for a chosen set of sensor