Stepped-frequency radar target recognition with random forests

Radar target recognition with Random Forests (RF) and using stepped-frequency radar features is the focus of this paper. Recent comparative studies between RF and convolutional neural networks (CNN) showed that RF yields reliable robust target recognition results with relatively fast training and testing time. The appeal of RF is that they can be implemented in parallel and have far fewer tunable parameters than CNN. In addition to providing measures of variable significance, and permitting differential class weighting, RF can help with imputation of missing data [1]. These RF properties make them a good target recognition alternative tool especially in scenarios where the data is occluded, or corrupted with extraneous scatterer, or when the target signature at certain azimuth position changes drastically compared to other likely positions (or aspect angles). This paper uses real radar data of commercial aircraft models recorded in a compact range. The results show that RF offers a fast and reliable alternative for target recognition systems especially under realistic radar operating conditions.