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Table 3 Performance comparison, single target (S) and multiple close targets (M) in noise, \(P=6\)

From: A neural network framework for binary classification of radar detections

Detector (trained on) S: \(P_{\mathrm{D}}\) S: \(P_{\mathrm{FA}}\) M: \(P_{\mathrm{D}}\) M: \(P_{\mathrm{FA}}\)
NN (N) O1, 50x1 0.76 \(5.78\times 10^{-8}\) 0.34 \(3.92\times 10^{-8}\)
NN (N) O2, 50x1 0.80 \(5.78\times 10^{-8}\) 0.51 \(1.96\times 10^{-8}\)
NN (N) O3, 50x1 0.82 \(9.64\times 10^{-9}\) 0.78 0
NN (N) O1, 50x2 0.76 \(7.52\times 10^{-7}\) 0.64 \(4.61\times 10^{-7}\)
NN (N) O2, 50x2 0.81 \(8.67\times 10^{-7}\) 0.77 \(3.92\times 10^{-7}\)
NN (N) O3, 50x2 0.82 \(3.95\times 10^{-7}\) 0.80 \(1.07\times 10^{-7}\)
NN (N+C) O1, 50x1 0.70 0 0.41 \(9.81\times 10^{-9}\)
NN (N+C) O2, 50x1 0.79 0 0.63 0
NN (N+C) O3, 50x1 0.79 \(1.92\times 10^{-8}\) 0.70 \(9.64\times 10^{-9}\)
NN (N+C) O1, 50x2 0.72 \(2.89\times 10^{-8}\) 0.60 \(2.35\times 10^{-7}\)
NN (N+C) O2, 50x2 0.79 \(2.89\times 10^{-8}\) 0.75 \(2.02\times 10^{-7}\)
NN (N+C) O3, 50x2 0.82 \(5.01\times 10^{-7}\) 0.79 \(5.11\times 10^{-7}\)
CMSO-CFAR 0.82 \(2.43\times 10^{-3}\) 0.82 \(2.38\times 10^{-3}\)
SO-CFAR 0.77 \(1.19\times 10^{-5}\) 0.62 \(1.10\times 10^{-5}\)
GO-CFAR 0.74 \(3.85\times 10^{-8}\) 0.09 \(1.96\times 10^{-8}\)