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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]

Micro-Doppler (m-D) effect, induced by the rotation of rotor blades, provides an important signature to discriminate between small unmanned aerial vehicles (UAVs) and other aircrafts or birds in remote surveillance. Compared with the Doppler signal induced by the translation, m-D signal, however, is rather weak and consists of multiple frequency components. In this paper, empirical mode decomposition (EMD) algorithm is applied to addressing the mode-mixing problem in the returned signal. Theoretically, Doppler features are consequently allocated in the first few intrinsic mode functions (IMFs). Rather, the partial components of the subsequent IMFs hold a similar property with the rotation signal. Those components are selected as the input data for the sparse recovery. With the sinusoidal frequency-modulated basis (SFMB), the essence of the recovery problem is converted into 1-D parameter optimization. Then, phase orthogonal matching pursuit (POMP) method is developed for the sparse solution. The proposed method is contrasted with the prevailing approach to solving the mode-mixing problem. Simulation results confirm the theoretical analysis, showing the feasibility in the estimation of m-D frequency. The preliminary findings from the measured data suggest that the proposed method has a potential application in the identification of small UAVs. [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]