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Proceedings Paper

Needle picking: a sampling based track-before-detection method for small targets
Author(s): Fatih Porikli
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Paper Abstract

We present a computationally efficient track-before-detect algorithm that achieves more than 50% true detection at 10-6 false alarm rate for pixel sized unknown number of multiple targets when the signal-to-noise ratio is less than 7dB. Without making any assumptions on the distribution functions, we select a small number of cells, so called as needles, and generate motion hypotheses using the target state transition model. We accumulate cell likelihoods along each hypothesis in the temporal window and append the accumulated values to the corresponding queues of the cell positions in the most recent image. We assign a target in case the queue maximum is greater than a threshold that produces the specified false alarm rate.

Paper Details

Date Published: 15 April 2010
PDF: 9 pages
Proc. SPIE 7698, Signal and Data Processing of Small Targets 2010, 769803 (15 April 2010); doi: 10.1117/12.850452
Show Author Affiliations
Fatih Porikli, Mitsubishi Electric Research Labs. (United States)


Published in SPIE Proceedings Vol. 7698:
Signal and Data Processing of Small Targets 2010
Oliver E. Drummond, Editor(s)

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