The micro-Doppler effect of radar targets reflects the target motion attribute, which can provide important information for target recognition. Based on the micro-Doppler effect, the estimation of micro-motion parameters of the cone is of great significance in the field of anti-missile. However, the existing methods based on monostatic radar can only extract some micro-motion parameters, and the research on micro-Doppler properties is not in-depth enough. In the article, the micro-Doppler properties of the precession cone with fins are studied. Firstly, the motion model of the precession cone with fins is given. Subsequently, the frequency shift equation of the fin is studied, and a set of equations containing the relationship between the micro-motion parameters is constructed. The micro-motion parameters (precession angle, bottom radius, angle between conning vector and radar line of sight) are estimated by solving the set of equations. The effectiveness of the algorithm is verified by simulated data and measured data.

Radar imaging can provide important information of target for recognition. However, due to the limited azimuth resolution, it is difficult to improve the imaging quality of the existing imaging technology based on the uniform circular array (UCA), due to the Bessel function modulation in the frequency domain of the echo received by the UCA. A resolution-improved imaging method based on the uniform circular array antenna (UCA) is proposed. The time–frequency dual relationship between vortex electromagnetic echo and UCA echo is deduced; on this basis, the Bessel function modulation compensation method is constructed to realize the azimuth high-resolution imaging of the target. The algorithm process is based on a UCA echo, which is independent of vortex electromagnetic waves. Theoretical analysis and simulation show that, compared with the existing vortex electromagnetic wave radar imaging methods, the azimuth resolution of the proposed method is improved.

In Synthetic aperture radar (SAR) recognition tasks, due to its special imaging principle, SAR images acquired from different viewpoints contain target features that may carry a large amount of information. These target features from different viewpoints enrich the training data and also improve the target recognition performance. However, considering the relative difficulty of acquiring SAR images and their fine annotations, if recognition is forced by fusion of multiview features when raw data is scarce, feature redundancy will be formed, which in turn will lead to a decrease in recognition accuracy. To remedy the above problem, a multiview inter-class dissimilarity feature fusion (MIDFF) network is proposed in this study. The proposed network can simultaneously input images of two classes, one of which is a multiview input used to extract features from the same target at different views, and the other is input from other classes, which we call Input-heterogeneous, used to extract heterogeneous features. Firstly, a method for rapidly generating sufficient training data for MIDFF is proposed, which generates training data by repeatedly combining images from different views and classes to ensure that a large number of training inputs are available even when raw SAR images are scarce. Secondly, a method of calculating and enhancing of inter-class dissimilarity (ICD) features is proposed to increase the inter-class distance and improve the inter-class separability. Then, the ICD and multiview features are fused to increase the features learned by the network and reduce feature redundancy. Finally, a multiview heterogeneous weighted loss function is proposed, which combines the calculation of inter-class similarity and heterogeneous loss. Through the gradual convergence of the loss function, the inter-class similarity decreases as the loss decreases, which further improves the target recognition performance. Experimental results on MSTAR and Civilian Vehicle SAR datasets show that our proposed method performs better than the state-of-the-art methods within sample scarcity conditions.

Micro-Doppler is regarded as a unique signature of a target with micro-motions. The sophisticated recognition of the cone-shaped targets can be realized through the micro-Doppler effect. However, it is difficult to extract the micro-motion features perpendicular to the radar line of sight (LOS) effectively. In this paper, a micro-Doppler parameters extraction method of the cone-shaped targets is put forward based on the rotating antenna. First, a new radar configuration is proposed, in which an antenna rotates uniformly on a fixed circle, thus producing Doppler frequency shift. Second, the expression of the micro-Doppler frequency shift induced by the precession cone-shaped target is derived. Then, the micro-Doppler curves of point scatterers at the cone top and bottom are separated by the smoothness of the curves, and the empirical mode decomposition (EMD) method is utilized for the detection and estimation of the coning frequency. Finally, the micro-motion components perpendicular to the radar LOS are inverted by the peak of micro-Doppler frequency curve. Simulation results prove the effectiveness and robustness of the proposed method.

The vortex electromagnetic wave radar based on monostatic geometry can realize the imaging of a radar target in both range dimension and azimuth dimension by adjusting vortex electromagnetic wave modes, but it has no ability to distinguish the elevation of the radar target. To achieve three-dimensional imaging of the radar target, a three-dimensional imaging method of vortex electromagnetic wave based on bistatic geometry is proposed. The echo model of the target based on vortex electromagnetic wave in bistatic geometry is constructed firstly. The elevation resolution of the target is realized, and then the three-dimensional coordinate of the target is reconstructed. Moreover, the elevation resolution and the computational complexity of the proposed method are also analyzed. Simulation results demonstrate the effectiveness of the proposed method. The results also illustrate that the array element requirements and the computational complexity of presented algorithm are less than Multiple Signal Classification (MUSIC) algorithm.

Micro-Doppler is a unique characteristic of targets with micro-motions, which can provide significant information for target classification and recognition. However, the monostatic radar has the shortcoming of only obtaining the radial micro-motion characteristics. Although the vortex-electromagnetic-wave-based radar has the potential to obtain real micro-motion parameters, it has a high dependence on the mode number and purity of the orbital angular momentum, which greatly restricts its application in the micro-motion parameter extraction. To overcome the above problems, a new radar configuration based on the rotating interference antenna is proposed in this paper. Through the interference processing of the micro-Doppler curves of the rotating and fixed antenna, the curves containing the real micro-motion information of the target can be obtained. Then the real micro-motion characteristics of the spinning target can be reconstructed by the orthogonal matching pursuit algorithm. The effectiveness and robustness of the proposed method are validated by simulations.