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 radar network system can provide multi-band and multi-view target information, which helps improve target recognition ability. A space micro-motion targets recognition method based on radar network systems (RNS) with a dual-channel bidirectional gated recurrent unit (BiGRU)-Transformer-graph fusion (DC-BiGT-GF) network is proposed in this paper. Firstly, a temporal feature extraction subnetwork based on BiGRU-Transformer is utilized to process the real and imaginary parts of complex-valued radar cross section (RCS) in parallel to capture the local and global temporal dependencies. Secondly, a spatial feature extraction subnetwork is designed to extract the potential spatial dependencies, which integrates a predefined graph and an adaptive graph. In the real part channel, the Euclidian distance between radars is used to construct the adjacency matrix to represent the predefined graph structure, and the adaptive adjacency matrix is designed to learn the potential graph structure from end to end. To represent the frequency domain features, the phase difference is applied in the imaginary part channel to build a predefined adjacency matrix. Meanwhile, the adaptive adjacency matrix is calculated using cosine similarity to obtain the geometric features. Finally, extensive experiments show that the DC-BiGT-GF network can reliably recognize the space micro-motion targets under low SNR and low radar pulse repetition frequency conditions. Recognition accuracy is greatly improved compared to the baseline methods.

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.

A networked radar system composed of radars deployed in different locations and working in different frequency bands can provide multi-band and multi-view target information, which can effectively improve the recognition accuracy of space micro-motion targets. The feature acquisition ability of each part of the networked radar systems is different. To fully tap the potential information of networked radar systems, a multi-domain feature-level fusion method based on networked radar systems for space micro-motion targets recognition is proposed in this paper. Firstly, the multi-domain features dataset of the networked radar systems is constructed. Secondly, the feature extraction and adaptive fusion subnetwork is designed. Two linear attention modules, cross-covariance atttention (XCA) module and linear angular attention (LA-Att) module, are introduced to extract fine-grained features in parallel and efficiently. The cross-attention fusion module (CAFM) is introduced for adaptive feature fusion. Then, a spatial-temporal feature extraction subnetwork is designed. The temporal features are extracted by bidirectional gated recurrent unit (BiGRU). The spatial dependencies between nodes are captured by the dual graph fusion network (DGFN) which integrates the adaptive graph and the predefined graph. Finally, the spatial-temporal feature vector is input into the prediction layer to obtain robust recognition results. Extensive experiments have proved the effectiveness and robustness of the proposed method.

In inverse synthetic aperture radar image formation, conventional methods become ineffective under incomplete data condition, such as in sparse aperture scenario. Although sparse signal processing techniques have been introduced to improve the quality of ISAR image, off-grid problems and the structural characteristics of ISAR images still require further investigation to improve image quality. This paper proposes a sparse Bayesian learning method based on a one-dimensional off-grid coupled structure to effectively capture the structural prior information of ISAR images and correct off-grid errors simultaneously. First, an ISAR signal model with one-dimensional off-grid error compensation is established through first-order Taylor expansion. Second, within the Bayesian framework, the prior features of ISAR images are considered through a 1D coupled structural model. Finally, to achieve efficient inference, generalized approximate message passing (GAMP) is used to infer model variables. Experimental results on both simulated and real-world data demonstrate that the proposed algorithm can effectively preserve the structural features of ISAR images and correct off-grid errors in the case of incomplete data, while maintaining high computational efficiency.