In this paper, we propose a novel decision fusion algorithm for target tracking in forward-looking infrared image sequences recorded from an airborne platform. An important part of this study is identifying the failure modes in this type of imagery. Our strategy is to prevent these failure modes from developing into tracking failures. The results furnished by competing ego-motion compensation and tracking algorithms are evaluated based on their similarity to a target model constructed using the weighted composite reference function.

We propose a novel decision fusion algorithm for target tracking in forward-looking infrared (FLIR) image sequences recorded from an airborne platform. The algorithm allows the fusion of complementary ego-motion compensation and tracking algorithms to estimate the position of the target in the current frame among a sequence of frames of FLIR imagery. We identified three modes that contribute to the failure of the tracking system: (1) the sensor ego-motion failure mode, which causes the movement of the target beyond the operational limits of the tracking stage; (2) the tracking failure mode, which occurs when the tracking algorithm fails to determine the correct location of the target in the new frame; (3) the reference-image distortion failure mode, which happens when the reference image accumulates walkoff error, especially when the target is changing in size, shape, or orientation from frame to frame. The strategy in our design is to prevent these failure modes from producing tracking failures. The overall performance of the algorithm is guaranteed to be much better than any individual tracking algorithm used in the fusion. One important aspect of the proposed algorithm is its recoverability: the ability to recover following a failure at a certain frame. The experiments performed on Army Missile Command AMCOM FLIR data set verify the robustness of the algorithm. (c) 2005 Society of Photo-Optical Instrumentation Engineers.

An analytic solution of the problem of electromagnetic scattering by a dielectric spherical scatterer resting on, or partially buried in, an infinite perfectly conducting ground plane is approximated by partially truncated sphere and is formulated using the method of images. The scattered field coefficients are solved exactly so that the scattered field can be evaluated everywhere. In particular, the scattering cross section can be calculated as a function of the sphere radius and permittivity as well as the truncated sphere distance for any specified angle of incidence. The solution of this problem is relevant to analyze the scattering by complex three-dimensional bodies, plastic mines, icebergs, rough surfaces, etc., in which the flat background can be modeled by the ground plane while the complex body can be simulated by a sphere or a system of spheres partially truncated and resting on the ground plane. In order to solve the inverse scattering problem, we employ a radial basis function network to take the scattered field complex coefficients for the TE and TM polarization case as the network inputs to predict the three outputs of the electrical radius, burial distance, and relative permittivity of the sphere. The trained network is able to retrieve the three aforementioned parameters from new data which is different from the learning data.

A near real-time invariant multi-target tracking algorithm based on the fringe-adjusted joint transform correlator (FJTC) technique is proposed for forward looking infra-red (FLIR) image sequences. The proposed FJTC based tracking approach uses a modified synthetic discriminant function (SDF) concept together with an efficient camera motion compensation technique to accommodate the problem of target signature variation due to 3D distortions and noise. The proposed technique can track small objects comprising of only a few pixels and is capable of compensating the high ego-motion of the sensor. The robustness of the proposed technique is demonstrated with computer simulation performed on sequences of real life FLIR imagery taken from an airborne moving platform.

In this paper, we propose a novel decision fusion algorithm for target tracking in forward looking infrared (FLIR) image sequences recorded from an airborne platform. The algorithm allows the fusion of complementary ego-motion compensation and tracking algorithms. We identified three modes that contribute to the failure of the tracking system: (1) the sensor ego-motion failure mode, which causes the movement of the target more than the operational limits of the tracking stage; (2) the tracking failure mode, which occurs when the tracking algorithm fails to determine the correct location of the target in the new frame; (3) the distortion of the reference image failure mode, which happens when the reference image accumulates walk-off error, specially when the target is changing in size, shape or orientation from frame to frame. The proposed algorithm prevents these failure modes from developing unrecoverable tracking failures. The overall performance of the algorithm is guaranteed to be much better than any individual tracking algorithm used in the fusion. The experiments performed on the AMCOM FLIR data set verify the robustness of the algorithm.

Presented in this paper is a fringe-adjusted joint transform correlator (FJTC) based invariant target tracking of forwar looking infra-red (FLIR) image sequences. The proposed FJTC based tracking approach employed a modified synthetic discriminant function (SDF) concept together with an efficient camera motion compensation technique to accommodate the problem of target signature variation due to in-plane/out-of-plane rotations, scale variations, noise, and bad frames. The proposed technique can track small objects comprising of only a few pixels and is capable of compensating the high ego-motion of the sensor. The robustness of the proposed technique is demonstrated with computer simulation performed on sequences of eal life FLIR imagery taken from an airborne moving platform.