Many
factors contribute to data imperfections in distributed decision making
environments. When large networks of inexpensive, multiple sensor modalities
are employed for detection purposes, their reliability, resolution,
sensitivity, sampling frequency, sensor proximity to the detected events,
and background noise can cause significant inaccuracies in gathered
information. Indicators derived from databases or subjective expert opinions
used to complement the sensor data may also contribute to such
imperfections. Moreover, often the situation under observation is inherently
uncertain. Prior information or conditional probability distributions are
not available and improper initial assumptions or interpolations can also
weaken the integrity of the decision making process.
Surveillance applications often
call for observing sensor data and various other indicators spanning over
multiple modalities. Here the term “multiple modalities” refer to different
types of measurements, i.e., metal from a vehicle detected using a
magnetometer, an indication of suspicious activity using a database etc.
Gathering such data over time and making inferences based on the ‘frequency’
characteristics of certain events can sometimes uncover a key piece of
information. For example, a periodically occurring pattern of an event
characterized by a particular set of sensor modalities may indicate an
imminent security threat in a homeland security application. Two main issues
need to be addressed in this context:
(a) How can we model imperfect data from multiple sensor modalities during
information processing?
(b) How can we make direct
inferences on the ‘frequency’ characteristics of events of interest?
In this
research, we integrate
Dempster-Shafer (DS) belief theory with discrete time filtering techniques
to address these two issues. The novel Evidence Filtering method presented
here is capable of fusing temporally ordered information from multiple
sensor modalities to directly infer on the frequency characteristics of
events. To our knowledge, no single strategy capable of providing inferences
in the frequency domain of events based on data from multiple sensor
modalities is yet available.
The advantage of using DS
theory to model evidence lies in its ability to conveniently represent a
wide variety of data imperfections. It has been extensively used in
surveillance and security applications in the past, and provides an
excellent framework to model imperfect data derived from multiple sensor
modalities. Moreover, this approach is ideal for the present context since
it is directly extendable to accommodate heterogeneous sources. The
advantages in DS theoretic methods become evident when the assumptions
typical of a Bayesian approach (e.g., conditional independence, availability
of priors, etc.) are difficult to justify.
Further details about this
research can be found in the following publications:
·
D. A.
Dewasurendra, P. H. Bauer, and K. Premaratne, "Evidence Filtering", IEEE
Transactions on Signal Processing, 2007, Accepted for publication.
·
D. A.
Dewasurendra,
P. H. Bauer, and K. Premaratne, "Distributed evidence filtering: The
recursive case", in Proceedings of the 2006 IEEE International Symposium on
Circuits and Systems (ISCAS '06), Kos, Greece, May 2006.
[PDF]
[PPT]
[BibTex]
·
D. A. Dewasurendra, P. H. Bauer, and K. Premaratne, "Distributed evidence
filtering in networked embedded systems", in Networked Embedded Sensing and
Control, ser. Lecture Notes in Control and Information Sciences, P. J.
Antsaklis and P. Tabuada, Eds. Springer-Verlag, 2006, vol. 331, pp. 183–198.
[PDF]
[PPT]
[BibTex]
