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Classification modes aimed for slow events, such as human gait, using micro-Doppler measurements are long, and therefore not suitable for use during time critical radar operation. When these modes are interruptible by other radar modes they might become more acceptable. In this paper, sparse signal processing, in particular the SL0-algorithm, is investigated for interpolating interrupted radar measurements which subsequently can be used for classification of human gaits. The performance of a k-NN classifier with PCA-based feature extraction was improved significantly when the data were interpolated as compared to using the interrupted data without interpolation.
Rossum et al. (Mon,) studied this question.