Efficient Detection of Small Moving Objects

A signal processing problem encountered with many sensor systems having a wide field-of-view is detection of small, unresolved objects moving in a straight line amid stationary clutter. The wide field-of-view combined with the need to accurately pinpoint object positions imply that these sensors must have hundreds of thousands of samples in their output. To process this amount of data in a timely fashion, computationally efficient algorithms are a necessity. In this report, a computationally efficient set of algorithms is described for detecting satellites, meteorites, and other moving objects using data from an optical telescope charge-coupled device (CCD) focal plane in the MIT Lincoln Laboratory Demonstration Surveillance System (DSS). The trade-off of reduced detection sensitivity for lower computational cost in the algorithm is quantitatively discussed. Major techniques employed are: 1. Sample normalization by temporal mean and standard deviation to suppress clutter. 2. Maximum value projection to reduce the dimensionality of the data. 3. A two-stage matched filter detector which first nominates and then confirms signal candidates. 4. Two-dimensional binary velocity filtering. The techniques should have practical application to other wide field-of-view sensors where moving object detection is important.