Polaris Sensor Technologies, Inc. is identifying target pixels in IR imagery at signal to noise (SNR) ranges from 1.25 to 3 with a mixed set of algorithms that are candidates for next generation focal planes. Some of these yield less than 50 false targets and a 95% probability of detection in this low SNR range. What has been discovered is that single frame imagery combined with IMU data can be input into a host of algorithms like Neural Networks and filters to isolate signals and cull noise. Solutions for nonlinear thresholding approaches can be solved using both genetic algorithms and neural networks. What is being addressed is how to implement these approaches and apply them to point target detection scenarios. The large format focal planes will flood the down stream image processing pipelines used in real time systems, and this team wonders if data can be thinned near the FPA using one of these techniques. Delivering all the target pixels with a minimum of false positives is the goal addressed by the group. Algorithms that can be digitally implemented in a ROIC are discussed as are the performance statistics Probability of Detection and False Alarm Rate. Results from multiple focal planes for varied scenarios will be presented.
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