Anomaly gamma spectra detection based on principal component analysis and the Mahalanobis distance

doi:10.16511/j.cnki.qhdxxb.2017.22.045

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Abstract A principal component analysis (PCA) and Mahalanobis distance (MD) based anomaly gamma spectra detection method is developed to improve the reliability of radiation scanning of goods and human bodies. This method first extracts all the principal components (PCs) of large numbers of benign gamma spectra by PCA and selects several largest PCs to form a subspace. The algorithm then projects the benign, unknown gamma spectra to this subspace, calculates their MDs, and completes the anomaly detection by comparing these MDs. Monte Carlo simulations and actual tests show that the method is reliable and effective when the subspace has more than 99% of the original information.

Keywords gamma spectra anomaly detection artificial intelligence principal component analysis Mahalanobis distance

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Issue Date: 15 August 2017

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	ZHAO Ri
	LIU Liye
	LI Junli

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ZHAO Ri,LIU Liye,LI Junli. Anomaly gamma spectra detection based on principal component analysis and the Mahalanobis distance[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(8): 826-831.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2017.22.045 OR http://jst.tsinghuajournals.com/EN/Y2017/V57/I8/826

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