Object recognition radar system for partially reconstructed target image

For the purpose of Radar Object Recognition (OR) system for real life scenarios where only a partially reconstructed image of the Object Under Test is available, an OR UltraWideband (UWB) Radar system is proposed. Conventional OR radar systems based on vector machines or neural networks result in a high recognition rates even for incomplete object images, but are not suitable for OR in a real time scenario due to the vast computational load. Hence the OR radar system proposed in this paper is based on a statistical approach employing a Bayesian detector and seven Object Recognition features with low mathematical and computational complexity. Furthermore, the proposed OR features are extracted from polarimetric radar images acquired by two imaging methods. Experimental validations are performed with an alphabet of twelve complex objects, a M-sequence UWB Radar device (4.5 GHz–13.5 GHz) and two compact dual-polarized Ultra-Wideband antennas.