Dictionary learning for classification of indoor micro-Doppler signatures across multiple carriers

Vishwakarma, Shelly and Ram, Shobha Sundar (2017) Dictionary learning for classification of indoor micro-Doppler signatures across multiple carriers. In 2017 IEEE Radar Conference, RadarConf 2017. IEEE. pp. 992-997 . (doi:10.1109/RADAR.2017.7944348).

Abstract

Micro-Doppler signatures of dynamic targets such as humans, animals and vehicles are very effective feature vectors for classification based on machine learning algorithms. In the existing works, the test data have been measured in nearly identical operating conditions to the training data that were gathered for the classifiers. However, this assumption may be violated in real life scenarios. In this work, we demonstrate that classification based on sparsity based dictionary learning can overcome this limitation. Here, we learn unique target class dictionaries from micro-Dopplers gathered at multiple carriers. Then we test the classifier using data gathered at another carrier (distinct from those used for training).We test the performance of the classification algorithm for both simulation and measurement data. Our results show a classification accuracy of 99% and 89% for simulated and measurement data respectively.

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Contributors

Author: Shelly Vishwakarma

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