A miniaturized low-profile ultrawideband antipodal Vivaldi antenna (AVA) array loaded with edge techniques is presented to enhance mutual coupling. Originating from a conventional AVA, the edges of the adjacent elements are overlapped with shorted pins loaded to expand the low operating band, suppress the in-band resonance and achieve miniaturization. The edge truncation effect of the E-plane array is subsequently improved by adding auxiliary radiating arms and a shorted strip at the end. Eventually, an 8×8 finite array without any complicated feeding mechanism or transition is constructed to verify the design. The measured operating band of the AVA array is 0.9–12 GHz (13.3:1), and the measured active voltage standing wave ratio (VSWR) is less than 3 when scanning up to ±60° in both the E- and H-planes, implying its wideband and wide-angle scanning capability. Owing to its compact size, ultrawide band, wide-angle scanning and simple feeding structure, the proposed AVA array shows promise for application in large-scale arrays and communication systems.

Radar cross section (RCS) sequences, an easy-to-obtain target feature with small data volume, play a significant role in radar target classification. However, radar target classification based on RCS sequences has the shortcomings of limited information and low recognition accuracy. In order to overcome the shortcomings of RCS-based methods, this paper proposes a spatial micro-motion target classification method based on RCS sequences encoding and convolutional neural network (CNN). First, we establish the micro-motion models of spatial targets, including precession, swing and rolling. Second, we introduce three approaches for encoding RCS sequences as images. These three types of images are Gramian angular field (GAF), Markov transition field (MTF) and recurrence plot (RP). Third, a multi-scale CNN is developed to classify those RCS feature maps. Finally, the experimental results demonstrate that RP is best at reflecting the characteristics of the target among those three encoding methods. Moreover, the proposed network outperforms other existing networks with the highest classification accuracy.

Aiming at the problem of micro-motion signal separation and micro-Doppler extraction of the precession target, a new method based on singular value decomposition (SVD) and joint approximate diagonalization of eigen-matrices (JADE) is proposed in this paper. Firstly, the micro-motion model of space precession target is established, and the micro-Doppler and scattering characteristics are analyzed to establish the echo signal model of the target. Secondly, through simplifying the signal model of scattering point and building the signal matrix of different signal lengths, the singular value ratio sequence is constructed by the method of SVD to estimate the precession period of the target. Thirdly, the singular vectors of different observation periods are extracted, the observation matrix is constructed, and then the JADE algorithm is adopted to separate the micro-motion signal of each scattering point. Finally, the clustering analysis is used to denoise the time-frequency graph and the centroid calculation is employed to extract the micro-Doppler of each scattering point. Simulation results show that this method of good robustness can effectively separate and extract the micro-Doppler of the scattering points.

To achieve the estimation of key parameters of phase-coded signals under low sampling rate conditions, a detailed derivation and analysis of the signal after anti-aliasing filtering were conducted. The analysis shows that when the signal is subjected to a certain frequency offset and analyzed using a fixed-scale Haar wavelet transform, the integral value reaches a local minimum when the frequency offset is equal to the carrier frequency. Moreover, the envelope spectrum of the signal exhibits a strong discrete spectral line at the frequency corresponding to the symbol rate, which enables the estimation of the carrier frequency and symbol period. When estimating the signal reception delay, considering the periodic modulation characteristics of the signal, the symbol period was incorporated into the Finite Rate of Innovation (FRI) sampling theory. An improved periodic annihilating filtering method was proposed. This method transforms the estimation of individual unknown pulse positions in the annihilating filter into the estimation of the common delay of periodic pulses, thereby significantly enhancing the estimation accuracy. Additionally, the symbol period is utilized to denoise the signal through singular value decomposition, thus completing the delay estimation. Simulation experiments have demonstrated the effectiveness of the proposed method.

The micro-Doppler (MD) signature, an effect that is induced by micro-motion, characterizes the rich motion information of targets, making it a valuable tool for target classification and recognition. However, the MD structure is always destroyed for radar targets with translational motion. In this letter, we propose an end–to–end translational motion compensation network based on a spatial transformer network (STN). First, the effect of the translational motion on the radar echo and its time-frequency graph (TFG) is modeled, so that translational motion compensation is regarded as an image spatial transformation task. Then, a two-branch convolutional network based on residual modules is designed to locate the transformation parameters, and the TFG containing only MD is obtained using a grid generator and sampler. Finally, the simulation results demonstrate that the proposed algorithm has high accuracy and stability.

Radar-based space target recognition is crucial for the space target defense system. Employing micro-motion features as a means to differentiate space targets has proven to be effective. However, most existing space target recognition methods based on deep neural networks are difficult to analyze the underlying dependence between the four radar signal variables of time, range, frequency, and power. In this study, a framework for space target recognition is proposed, which utilizes a multi-domain radar tool called the fourdimensional (4D) Range-Frequency-Time-Power radar data cube to capture micro-motion features. The radar echoes are first transformed into a series of highresolution RD sequences. Next, the estimation method for scattering point information is applied to acquire four types of information related to the targets, which are subsequently used to generate the 4D radar data cube. The resulting 4D radar data cube is then inputted into a recently developed coordinate-temporal attention network (CTA-Net) to extract features and perform micro-motion classification. Finally, an electromagnetic (EM) computation dataset is collected to validate the performance of CTA-Net. This research thoroughly investigates multiple crucial parameters of the dataset on recognition performance. Additionally, the robustness of the proposed framework is demonstrated through a wide range of experimental results