This paper presents a LADAR recognition challenge problem. There are two unique components of this challenge
problem: the focus on condence estimates and the explicit model of the recognition output as a function
of operating conditions (OCs). To promote the development of exploitation algorithms that map OCs onto
condence estimates, a set of synthetic data has been generated that explicitly samples specic OC dimensions:
resolution, noise, aspect, obscuration, and library. Submitted algorithms will be evaluated based on their ability
to correctly estimate the condence based on OC knowledge. Tools will be provided to generate performance
metrics on data sets. Participants will submit their algorithms for evaluation on a sequestered set of data. The
resulting performance metrics will be made available online so participants can evaluate their algorithms relative
to their peers.
In this paper we present an overview of the National Image Interprability Rating Scale (NIIRS) for SAR im-
agery. We map basic SAR image formation parameters into the NIIRS via an information theoretic framework.
Preliminary results obtained from a pilot study are presented for human interpretablity of various SAR im-
ages. Extensions to this work which include sensor exploitation algorithms and integration within the Pursuer
environment are outlined .
In this paper, we compare the information-theoretic metrics of the Kullback-Leibler (K-L) and Renyi (α) divergence
formulations for sensor management. Information-theoretic metrics have been well suited for sensor management as they
afford comparisons between distributions resulting from different types of sensors under different actions. The difference
in distributions can also be measured as entropy formulations to discern the communication channel capacity (i.e.,
Shannon limit). In this paper, we formulate a sensor management scenario for target tracking and compare various
metrics for performance evaluation as a function of the design parameter (α) so as to determine which measures might
be appropriate for sensor management given the dynamics of the scenario and design parameter.
In many cases, tracking ground targets can be formulated as a nonlinear filtering problem when terrain and road constraints are incorporated into system modeling and polar coordinate is used. Furthermore, when tracking ground maneuvering targets with an interacting multiple model (IMM) approach, a non-Gaussian problem exists due to an inherent mixing operation. A multirate interacting multiple model particle filter (MRIMM-PF) is presented in this paper to effectively solve the problem of nonlinear and non-Gaussian tracking, with an emphasis on computational savings.