| Summary: | Approved for public release; distribution is unlimited === In many remote sensing applications, the area of a scene sensed by a single pixel can often be measured in square meters. This means that many objects of interest in a scene are smaller than a single pixel in the resulting image. Current tracking methods rely on robust object detection using multi-pixel features. A subpixel object does not provide enough information for these methods to work. This dissertation presents a method for tracking subpixel objects in image sequences captured from a stationary sensor that is critically sampled spatially. Using template matching, we estimate the maximum a posteriori probability of the target state over a sequence of images. A distance transform is used to calculate the motion prior in linear time, dramatically decreasing computation requirements. We compare the results of this method to a previously state-of-the-art track-before-detect particle filter designed for tracking small, low contrast objects using both synthetic and real-world imagery. Results show our method produces more accurate state estimates and higher detection rates than the current state of the art methods at signal-to-noise ratios as low as 3dB.
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