The rotation of rotor blades on small unmanned aerial vehicles (UAVs) introduces micro-Doppler (m-D) signatures to discriminate UAVs from other aircrafts or birds. Compared with the Doppler signal induced by the translation of the platform, the m-D signal, however, is rather weak due to the small radar cross section (RCS) of rotor blades. Moreover, the m-D signal always consists of multiple frequency components, most of which are induced by the vibration of the platform. This paper focuses on the extraction of the rotation signal via the empirical mode decomposition (EMD) algorithm, which would decompose an arbitrary signal into a series of intrinsic mode functions (IMFs). The constitution of IMF is investigated by using the partial differential equations (PDEs) to analyze the distribution of the extrema. For separating the rotation signal, the essence is to unify the frequency of extrema in the corresponding IMF exhibiting the rotation features. First, the fitting Doppler signal is constructed from the data with the Doppler property in the IMF. Second, a criterion is proposed to establish the masking signal, after which the rotation components are extracted from the residual m-D signal via another EMD. Simulations are carried out to demonstrate the effectiveness of the proposed method in solving the mode-mixing problem. The results derived from the measured data validate the feasibility of the proposed approach in the identification of small UAVs.

Robust radar detection for small unmanned aerial vehicles (UAVs) is a challenging problem, as UAVs tend to fly at slow speed and low altitude, with a small radar cross section. Those properties may lead to difficulties in separating echoes from a significant clutter response. In this case, micro-Doppler (m-D) effect induced by the rotation of the rotor blades would be another preferable signature to enhance the discrimination between ground clutter and UAVs returns, as well as associated correct labels to small UAVs among others (such as birds). However, the detection presents two stubborn problems. First, the m-D signal always consists of multiple components, one of which is induced by the vibration of the platform. It is inevitable that the estimation precision would be affected by the m-D interference. Second, the m-D components would be rather weak. It could not always be observed due to the shelter of Doppler signal induced by the translation of the platform. In this paper, we propose the cyclostationary phase analysis (CPA) to estimate the m-D parameters for radar detection of small UAVs. This method utilizes the phase term of the returned signal as the input of the cyclostationary analysis. It could simplify the procedure of the parameter estimation, as well as reducing the m-D interference. Moreover, since the phase term of the Doppler components is not cyclostationary, the CPA could eliminate the impact of Doppler signal. Simulations and filed experiments are provided to showing an outranking performance than the existing methods in estimation precision.

Vehicles detection via frequency modulated continuous wave radar has attracted a growing interest in recent years. However, the estimation of the distance and velocity of vehicles poses challenges under the complex urban scenes, as the returned signal is contaminated by the swaying trees and the buildings or the pedestrians. In this paper, an efficient method for vehicles detection is proposed to reduce the interference, especially caused by the moving foliage. Gaussian mixture model is established for the returned signal in frequency domain to segment foreground targets from the clutter background. Then, the motional parameters are obtained from the foreground curve extracted by Hough transform. The proposed method has a significant improvement in increasing the accuracy of the parameter estimation of vehicles in complex urban scenes. In addition, field experiments are provided to showing an outranking performance in comparison with conventional methods without decreasing the detection rate.

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.

This paper intends to address the long-time coherent integration (LTCI) of high maneuvering targets in low signal-to-noise (SNR) environments with high computational efficiency and accuracy. The high-order motion parameters estimation, considering the acceleration and jerk, is modeled as the under-estimated linear regression and the complex-field Bayesian compressive sensing (BCS) algorithm is introduced to resolve the sparse recovery. To correct the high-order range migration (RM) and reduce the order of Doppler frequency migration (DFM), the adjacent cross correlation (ACCF) is applied. An effective method based on non-coherent integration is proposed to extract the self-term from ACCF result. Further, with the proper design of sensing matrix, the acceleration and jerk motion parameters are estimated by sparse reconstruction based on the complex-field BCS. Compared with the traditional methods based on time-frequency transform, like Lv's distribution (LVD), the proposed BCS algorithm is free from the interference of cross-terms and maintains the super-resolution ability, which provides better performance in parameters estimation and multi-targets discrimination. Finally, the numerical experiments validate the advantages of the proposed method in motion parameters estimation, super-resolution ability and computational efficiency.

Micro-Doppler (m-D) effect, induced by the rotation of rotor blades, introduces significant characteristics to identify small unmanned aerial vehicles (UAVs) in remote surveillance. As opposed to the Doppler signal induced by the translation, m-D signal is comparatively weak and consists of multiple frequency components. In this letter, we propose synchrosqueezing phase analysis (SPA) for the extraction of rotation signal with the multichannel radar. Based on the proposed signal model, this new method not only enables multivariate denoising and sharpening time-frequency (TF) representation, but also concentrates on the energy of rotation signal for the separation. Simulations are employed to demonstrate the validity of the proposed method in extracting the m-D features. Applications on field data further prove the potential in delineating m-D characteristics with higher precision and render that this technique is promising for the identification of small UAVs.