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A dual-polarized radar system is utilized for non-contact sensing applications involving rotational motion. Polarization-centric methods are employed to sense and characterize responses from an electric motor with various bearing faults and to sense responses from a high-velocity fan. Conventional characterizations, such as power spectral densities and micro-Doppler responses, are compared with polarization-based statistical approaches such as polarization entropy and a newly proposed micro-Entropy approach inspired by the micro-Doppler approach. [ABSTRACT FROM AUTHOR]

In this article, an unmanned aerial vehicles detection system is proposed for an X-band ground-based surveillance frequency modulated continuous wave Doppler radar. Two novel features are directly extracted from the Doppler processing results without a time-frequency analysis. Experimental results on measured radar echo signals show that our system consistently outperforms the state of the art in terms of detection accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Sparse signal representation is used to coherently separate the main rotor blade components of a helicopter from the composite data of complex time series radar returns. Received signals of this type typically consist of returns from the rotating main and tail rotor blades, the helicopter body, and other residual components which may overlap in time and frequency, thus making it difficult for conventional time–frequency separation techniques to be applied for the signal separation. In the proposed algorithm, a sparse signal representation is applied with the use of tunable Q wavelet transform to construct the dictionary, and basis pursuit denoising is used for the signal reconstruction of the component of interest. The algorithm shows very effective separation of the main rotor blade component from the composite radar returns, which is demonstrated using both simulated and real radar data at X-band. [ABSTRACT FROM AUTHOR]

In the work presented here we propose a novel bicoherence-based method for the classification of aerial radar targets in automatic target recognition (ATR) systems. The possibility of classifying aerial targets using the micro-Doppler contributions caused by a jet engine or the rotor of a helicopter is studied. The method is based on classification features computed in the form of bicoherence estimates, as well as cepstral coefficients extracted from the micro-Doppler contribution contained in radar returns. The performance of the classification method developed is compared with the performance of common methods using high-resolution radar range profiles (HRRPs). Correct classification probability rates are computed for three different types of aerial targets. The benefits achieved by using bicoherence-based classification features are demonstrated and discussed. [ABSTRACT FROM AUTHOR]