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LASER BASED STAND-OFF DETECTION OF BIOLOGICAL AGENTS output is subsequently recombined into a two-class biological/non-biological output (anticipated to be required in any operational generic detection system), it may result in less confusion between bio-simulants and fluorescent interferents. Figure 34 shows result of the 2-class SVM algorithm applied to a fluorescent cloud detection during a scanning trial. The sensitivity of the fluorescence detection is such that the discrimination has been achieved on clouds at ranges of up to 2 km, in real time, without the need for staring, at scan speeds up to 2°s -1 . 8.0 LWIR FIELD TEST RESULTS Figure 34: Display Screenshot with Cloud Detection. The traditional algorithmic approach to estimating the concentration of an aerosol cloud from LIDAR backscatter measurements requires prior knowledge of the wavelength dependent backscatter coefficients. In applications were those parameters are well characterized a priori the traditional approach is well suited. In the present application of detecting a biological threat cloud that was produced by an unknown source, using an unknown process, in the presence of a complex background, the traditional approach is not feasible. For these reasons it is necessary to estimate the spectral structure and concentration from the same data. The algorithm that has been developed in collaboration with Russell Warren of EO-Stat estimates the aerosol backscatter wavelength dependence and concentration range dependence in parallel [16]. It operates sequentially using a combination of Kalman filtering for the concentration and maximum likelihood for the backscatter. The backscatter and concentration estimates are used to construct a generalized likelihood ratio test statistic for the presence of aerosol whose distribution under the null hypothesis is well characterized. Once the presence of a material is detected and the spectrum (Figure 35) is estimated the discrimination algorithm is applied. Several discrimination algorithms were evaluated. These included the Linear Fisher Discriminant (LFD), Multi-Layer Perceptrons, and Support Vector Classifier (SVC). The SVC significantly outperformed the other approaches and the results are shown in Figure 36. 30 RTO-TR-SET-098
Non-Bio Bio LASER BASED STAND-OFF DETECTION OF BIOLOGICAL AGENTS Wavelength [from 9.3 to 10.7 microns] RTO-TR-SET-098 31 Relative Units Figure 35: Sample Backscatter Spectra of Some Biological Simulants and Interferents in the LWIR Region. The single shot measurement standard deviation is shown. SVC Statistic 1.5 1 0.5 0 -0.5 -1 -1.5 BA BG EH YP MS2 Kaolin Diesel Yellow Smoke Burning Brush Figure 36: LWIR Demonstrated Discrimination Results. Burning Wood
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