Target classification and recognition based on micro-Doppler radar signatures

The mechanical dynamics in addition to its bulk translation of the target or any structure on the target is called micro-motion, which yields new features in the target's signature that are distinct from its signature in the absence of micro-motion. Micro-motion evokes a frequency modulation in radar echo known as micro-Doppler (m-D) effect which may help to detect specific intrinsic structures of the target, leading a potential method to perform target discrimination and identification by extracting micro-Doppler features. At present, it has drawn a lot of attention to extract the micro-motion target's m-D information for target classification and identification. The various m-D classification approaches require first the extraction of salient features from the radar signal. The micro-Doppler usually manifests curves characteristic in the time-frequency (T-F) domain. Thus the features are calculated from the joint T-F domain. Most of these techniques treat the spectrogram in T-F domain as an image, and obtain features through some image processing techniques. In this paper, we investigated statistical classification and recognition methods for target classification using their micro-Doppler signatures. In our work, micro-Doppler signatures for targets represented by point scattering model with four different micro-motions (Vibration, Coning, Spinning, and Precession) are studied. We propose use of principle component analysis (PCA) and 2-D PCA as the data driven feature extraction approaches that captures vital statistics of the input at a reduced dimension. Simulation analysis by using the simulated data is performed to confirm the effectiveness of the proposal. Experiment results show that with the proposed methods, perfect classification of four different motions can be attained when training and testing set has data from different targets.