In the past few decades, the demand for reliable and robust systems capable of monitoring unmanned aerial vehicles (UAVs) increased significantly due to the security threats from its wide applications. During UAVs surveillance, birds are a typical confuser target. Therefore, discriminating UAVs from birds is critical for successful non-cooperative UAVs surveillance. Micro-Doppler signature (m-DS) reflects the scattering characteristics of micro-motion targets and has been utilized for many radar automatic target recognition (RATR) tasks. In this paper, the authors deploy local mean decomposition (LMD) to separate the m-DS of the micro-motion parts from the body returns of the UAVs and birds. After the separation, rotating parts will be obtained without the interference of the body components, and the m-DS features can also be revealed more clearly, which is conducive to feature extraction. What is more, there are some problems in using m-DS only for target classification. Firstly, extracting only m-DS features makes incomplete use of information in the spectrogram. Secondly, m-DS can be observed only for metal rotor UAVs, or large UAVs when they are closer to the radar. Lastly, m-DS cannot be observed when the size of the birds is small, or when it is gliding. The authors thus propose an algorithm for RATR of UAVs and interfering targets under a new system of L band staring radar. In this algorithm, to make full use of the information in the spectrogram and supplement the information in exceptional situations, m-DS, movement, and energy aggregation features of the target are extracted from the spectrogram. On the benchmark dataset, the proposed algorithm demonstrates a better performance than the state-of-the-art algorithms. More specifically, the equal error rate (EER) proposed is 2.56% lower than the existing methods, which demonstrates the effectiveness of the proposed algorithm.

In this paper, in order to reduce the probability of the radar waveform intercepted by the passive detection system, the time-bandwidth product of the radar waveform is increased, and the detection probability of the radar waveform to the target is improved. This paper tackles the holographic RF stealth radar and proposes a joint coding waveform based on the linear frequency modulation (LFM) waveform. Joint coding uses complementary codes to perform phase-coding, and combines the codewords optimized by genetic algorithm in order to perform discrete frequency-coding waveform. The joint coding waveform model is theoretically analyzed, and the ambiguity function, pulse compression and target detection probability of the joint coding waveform are obtained by numerical simulation. In addition, the complexity of the algorithm and the low probability of intercept (LPI) characteristic of the joint coding waveform are analyzed. The results show that the joint coding waveform has an approximate “pushpin” ambiguity function, ultra-low sidelobe characteristics, better RF stealth and target detection performance. Finally, it has good application prospects in the current battlefield environment.

Interferometric inverse synthetic aperture radar (InISAR) imaging is capable of retrieving three-dimensional target information from multi-channel inverse synthetic aperture radar (ISAR) images obtained by adjacent radars or antennas. The InISAR imaging of space targets is more difficult, compared with that observes short range targets with small radar systems. Considering the practical constraints when observing space targets with multiple large radars, this paper establishes a flexible InISAR imaging system which allows for non-perpendicular baselines and observation of targets under squint mode. Based on this, the generalized formulas for calculating scatterers' coordinates from measured interferometric phases are derived. Moreover, a pulse-level image registration method based on joint translational motion compensation is proposed to recover the coherence between multi-channel ISAR images, under condition of inconsistent pulse delays at different radar channels. With the proposed method, promising InISAR imaging results are obtained via two simulation experiments. The first experiment assumes ideal point scattering of the target, which verifies the high accuracy and computational efficiency of the proposed ISAR image registration method; the second experiment considers more practical target scattering using facet target model and physical optics algorithm, which demonstrates the possible deviation in InISAR imaging result caused by scatterer's anisotropic scattering and residual error of translational motion compensation. We believe that the derivation and simulation results in this paper would be helpful reference for practical InISAR imaging experiments.

We propose a new image registration method based on joint respective window sampling (RWS) and modified motion compensation (MMC) in an interferometric inverse synthetic aperture radar (InISAR) imaging system using two antennas. The causation and quantitative analysis of the offset between two ISAR images of different antennas along the baseline are analyzed. In the proposed method, the RWS method, according to the measured distance between the target and different antennas, compensates the offset in the range direction. The MMC method is adopted to eliminate the offset in the Doppler direction. Simulation results demonstrate that the offset between the two ISAR images can be compensated effectively, consequently achieving a high-quality three-dimensional InISAR image.

In real scenarios, radar systems must identify both known and unknown classes, referred to as open set recognition (OSR). However, most prototype-based OSR methods neglect the potential impact of the directional relationship between feature embeddings and prototypes on OSR performance, leading to open space risk. Besides, the superposition of diverse constraints results in the irregular shapes of known class clusters, making accurate fitting of these cluster edges a challenging task. The spatial distribution learning (SDL) with multivariate extreme value boundary (SDL-MEVB) is proposed to address the above issues. Firstly, we design the SDL by the fully mining and utilization of the latent representation information across the entire feature space. The proposed constraint refines the orientational distribution of feature embeddings and mitigates open space risk to the greatest extent. Secondly, extreme value theory (EVT) is utilized to model the marginal behavior of each single variable affecting the class cluster boundary. Furthermore, we analyze the mutual dependence between these variables and establish a multivariate model to accurately fit the class cluster boundary. The effectiveness of SDL-MEVB has been verified across different backbone networks using the measured HRRPs. The experimental results demonstrate that SDL-MEVB achieves state-of-the-art OSR performance among the implemented methods.