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| Paper: | SPTM-P13.9 |
| Session: | Detection and Classification |
| Time: | Friday, May 21, 15:30 - 17:30 |
| Presentation: |
Poster |
| Topic: |
Signal Processing Theory and Methods: Detection, Estimation, and Class. Thry & Apps. |
| Title: |
DATA SELECTION FOR DETECTION OF KNOWN SIGNALS: THE RESTRICTED-LENGTH MATCHED FILTER |
| Authors: |
Charles Sestok; Massachusetts Institute of Technology | | |
| Abstract: |
Data selection algorithms in detection search for a small subset of the available data that is sufficient for making an accurate decision. This paper considers data selection for detection of a known signal in colored Gaussian noise. In our model, the performance of the matched filter detector for a specific subset is parameterized by a quadratic form. Selection of the best subset leads to a combinatorial optimization problem using the quadratic form as the objective function. Simulations show that heuristic search algorithms often find good solutions for the selected subset. Additionally, if the noise has a banded covariance matrix, a dynamic programming algorithm finds the optimal solution for any subset size. |
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