A Novel Sense-through-Foliage Target Recognition Method Based on Sparse Representation

In this paper, a novel sparse representation (SR) based method for recognizing targets under different foliage scenarios is presented. Target echo signals of different targets in different scenarios measured by bistatic ultra-wideband (UWB) radar are used for scenarios and targets recognition. Particularly, two overcomplete dictionaries one for scenarios and one for targets are learned from measured real target echo signal waveforms via dictionary learning technique. The SR expresses an input signal as the linear combination of a small number of the learned dictionary elements, which are from the same category as the input signal. The SR of a test target echo signal can be achieved by solving an L₀-norm minimization problem. For targets recognition under multi-scenario, firstly, we locate the scenario of the targets with the overcomplete dictionary for scenarios via SR. Then, the types of the targets are recognized using the overcomplete dictionary for targets via SR. The effectiveness of the proposed approach is verified by experiments taken in the forest measurement. Experimental results on real measured data show that the proposed method achieves higher recognition accuracy.