| 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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