Low Signal-to-Noise Ratio Radar Target Detection Using Linear Support Vector Machines (L-SVM)
Ball, J. E. (2014). Low Signal-to-Noise Ratio Radar Target Detection Using Linear Support Vector Machines (L-SVM). Proc. IEEE RadarCon 2014. Cincinnati, OH: IEEE. 1291-1294. DOI:10.1109/RADAR.2014.6875798.
This paper examines target detection using a Linear Support Vector Machine (L-SVM). Traditional radars typically use a Constant False Alarm Rate (CFAR) processor to adaptively adjust the detection threshold based on the fast-time return signal. The SVM formulation uses the same block-diagram structure as the CFAR approach; however, data from the leading and lagging windows is directly used to classify each cell under test. The L-SVM method is compared to a Cell-Averaging CFAR (CA-CFAR) on simulated radar return signals with and without Swerling I targets. The results show that the L-SVM is able to detect very small SNR signals, while the CA-CFAR is unable to detect these signals below -10 dB SNR. In addition, the probability of detection and probability of false alarm for the L-SVM degrade much more gracefully than for the CA-CFAR detector for low-SNR targets.