Summary: IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, PART B 1
Localization­Based Sensor Validation Using The
Kullback­Leibler Divergence
Parham Aarabi, Member, IEEE
Abstract--- A sensor validation criteria based on the sensor's
object localization accuracy is proposed. Assuming that the true
probability distribution of an object or event in space f(x)
is known and a spatial likelihood function (SLF) #(x) for
the same object or event in space is obtained from a sensor,
then the expected value of the SLF E[#(x)] is proposed as a
suitable validity metric for the sensor, where the expectation
is performed over the distribution f(x). It is shown that for
the class of increasing linear log­likelihood SLFs, the proposed
validity metric is equivalent to the Kullback­Leibler distance
between f(x) and the unknown sensor­based distribution g(x)
where the SLF #(x) is an observable increasing function of the
unobservable g(x). The proposed technique is illustrated through
several simulated and experimental examples.
Index Terms--- Sensor validation, Kullback­Leibler divergence,
object localization, sound localization.

Source: Aarabi, Parham - Department of Electrical and Computer Engineering, University of Toronto

Collections: Computer Technologies and Information Sciences