2 research outputs found
An information-theoretic approach to data fusion and sensor management
The use of multi-sensor systems entails a Data Fusion and Sensor Management requirement
in order to optimize the use of resources and allow the synergistic operation of sensors.
To date, data fusion and sensor management have largely been dealt with separately and
primarily for centralized and hierarchical systems. Although work has recently been done
in distributed and decentralized data fusion, very little of it has addressed sensor management.
In decentralized systems, a consistent and coherent approach is essential and the ad
hoc methods used in other systems become unsatisfactory.
This thesis concerns the development of a unified approach to data fusion and sensor
management in multi-sensor systems in general and decentralized systems in particular,
within a single consistent information-theoretic framework. Our approach is based on considering
information and its gain as the main goal of multi-sensor systems. We develop a
probabilistic information update paradigm from which we derive directly architectures and
algorithms for decentralized data fusion and, most importantly, address sensor management.
Presented with several alternatives, the question of how to make decisions leading to
the best sensing configuration or actions, defines the management problem. We discuss the
issues in decentralized decision making and present a normative method for decentralized
sensor management based on information as expected utility. We discuss several ways of
realizing the solution culminating in an iterative method akin to bargaining for a general decentralized
system. Underlying this is the need for a good sensor model detailing a sensor's
physical operation and the phenomenological nature of measurements vis-a-vis the probabilistic
information the sensor provides. Also, implicit in a sensor management problem is
the existence of several sensing alternatives such as those provided by agile or multi-mode
sensors. With our application in mind, we detail such a sensor model for a novel Tracking
Sonar with precisely these capabilities making it ideal for managed data fusion. As an
application, we consider vehicle navigation, specifically localization and map-building. Implementation
is on the OxNav vehicle (JTR) which we are currently developing. The results
show, firstly, how with managed data fusion, localization is greatly speeded up compared
to previous published work and secondly, how synergistic operation such as sensor-feature
assignments, hand-off and cueing can be realised decentrally. This implementation provides
new ways of addressing vehicle navigation, while the theoretical results are applicable to a
variety of multi-sensing problems.</p
A Bayesian approach to optimal sensor placement
By "intelligently" locating a sensor with respect to its environment it is possible
to minimize the number of sensing operations required to perform many tasks.
This is particularly important for sensing media which provide only "sparse" data,
such as tactile sensors and sonar. In this thesis, a system is described which uses
the principles of statistical decision theory to determine the optimal sensing locations
to perform recognition and localization operations. The system uses a
Bayesian approach to utilize any prior object information (including object models
or previously-acquired sensory data) in choosing the sensing locations.</p