Needle Picking: A Sampling Based Track-Before-Detection Method for Small Targets

We present a computationally efficient track-before-detect algorithm that achieves more than 50% true detection at 0.000001 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 position 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. SPIE Defense and Security This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ©Mitsubishi Electric Research Laboratories, Inc., 2010 201 Broadway, Cambridge, Massachusetts 02139