Bayesian Spectroscopy and Target Tracking
Statistical analysis gives a paradigm for detection and tracking of weak-signature sources that are moving among a network of detectors. The detector platforms compute and exchange information with near-neighbors in the form of Bayesian probabilities for possible sources. This can shown to be an optimal scheme for the use of detector information and communication resources. Here, we apply that paradigm to the detection and discrimination of radiation sources using multi-channel gamma-ray spectra. We present algorithms for the reduction of detector data to probability estimates and the fusion of estimates among multiple detectors. A primary result is the development of a goodness-of-fit metric, similar to {chi}{sup 2}, for template matching that is statistically valid for spectral channels with low expected counts. Discrimination of a target source from other false sources and detection of imprecisely known spectra are the main applications considered. We use simulated NaI spectral data to demonstrate the Bayesian algorithm compare it to other techniques. Results of simulations of a network of spectrometers are presented, showing its capability to distinguish intended targets from nuisance sources.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- US Department of Energy (US)
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 15006295
- Report Number(s):
- UCRL-JC-143305; TRN: US200407%%192
- Resource Relation:
- Conference: 7th International Conference on Applications of Nuclear Techniques, Crete (GR), 06/17/2001--06/23/2001; Other Information: PBD: 1 May 2001
- Country of Publication:
- United States
- Language:
- English
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