Performance analysis through predictive modeling: application to an anomaly detection algorithm in operational scenarios

Eric R. Languirand; Darren K. Emge

doi:10.1117/12.2618125

30 May 2022 Performance analysis through predictive modeling: application to an anomaly detection algorithm in operational scenarios

Eric R. Languirand, Darren K. Emge

Author Affiliations +

Proceedings Volume 12116, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXIII; 1211606 (2022) https://doi.org/10.1117/12.2618125
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Understanding a system’s performance while operating under different scenarios is difficult because of the vast number of varying parameters that need to be accounted for. To mitigate some of the difficulty a model can be developed that provides some predictability in a system’s performance thereby reducing material usage and laboratory time. It is therefore prudent to understand these parameters and capture that information in order to increase the predictability of a system, especially prior fielding. Through modeling, we connect laboratory scale data with potential scenarios in the field to accomplish this. In this paper, we show that through the modeling of a combination of spectra and instrument operating characteristics we can provide a predictive capability of a system’s performance. Our anomaly detection algorithm can predict a limit of anomaly detection (LOAD) for potential scenarios and then compare them to actual data for validation of our predictive capability. We show similar LOADs in both simulation and actual data collected. We further develop our model to account for realistic field scenarios and evaluate changes in performance.

Conference Presentation

Citation Download Citation

Eric R. Languirand and Darren K. Emge "Performance analysis through predictive modeling: application to an anomaly detection algorithm in operational scenarios", Proc. SPIE 12116, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXIII, 1211606 (30 May 2022); https://doi.org/10.1117/12.2618125

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