Minimum divergence approaches for robust classification of ground moving targets

This work addresses the problem of automatic target recognition (ATR) using micro-Doppler information obtained by a low-resolution ground surveillance radar. Gaussian mixture models (GMMs) are used to represent the prior statistical information of threatening ground moving targets such as walking personnel and tracked or wheeled vehicles. A minimum divergence (MD) classification approach with a variety of distance measures is proposed. The proposed MD classification approach is robust with respect to modeling errors and can be efficiently used in low signal-to-noise (SNR) and training data deficient scenarios. The MD classifier is implemented using a variety of computationally efficient approximations of distance measures between GMMs. Performance of the MD classifier was analyzed using collected radar measurements and the influence of different distance measures and their approximations on classification performance is assessed. The proposed MD classifier outperforms the maximum likelihood (ML) classifier in low-SNR and training data deficient scenarios while providing a computationally efficient implementation.