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| Paper: | SPTM-P13.3 |
| 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: |
A GLRT AND BOOTSTRAP APPROACH TO DETECTION IN MAGNETIC RESONANCE FORCE MICROSCOPY |
| Authors: |
Pei-Jung Chung; University of Michigan, Ann Arbor | | |
| | José Moura; Carnegie Mellon University | | |
| Abstract: |
Magnetic resonance force microscopy (MRFM) is a technology that will potentially enable microscopy of molecules and proteins at atomic--scale detail. Physicists are pursuing MRFM and single electron spin microscopy (SESM). Many technological challenges exist for MRFM and SESM to deliver on the promise of ``visualizing'' a single electron spin. The forces of interest are in the subattoneNewton and attoneNewton range ($10^{-18}$N). In this paper we consider the problem in MRFM and SESM of detecting extremely weak signals buried in noise with SNR in the range of -15~dB to -40~dB. We describe a model that, although simplistic, captures the features of the problem.We present a GLRT and bootstrap approach that incorporates a bank of Viterbi algorithms, and show by simulations that, with physically realistic parameter values, the detector can achieve probability of detection $\beta=0.9$ with false alarm rate $\alpha=0.05$, at SNR$=-20$ dB. |
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