Multi-rotor aircraft have the advantages of a simple structure, low cost, and flexible operation in the unmanned aerial vehicle (UAV) family, and have developed rapidly in recent years. Radar surveillance and classification of the growing number of multi-rotor aircraft has become a challenging problem due to their low-slow-small (LSS) characteristics. Estimation of the blade number is an important step in distinguishing LSS targets. However, most of the current research on micro-motion parameters estimation has focused on the analysis of rotational frequency, length, and the initial phase of blades with a prior of blade number, affecting its ability to identify LSS targets. In this article, a micro-motion parameters estimation method for multi-rotor targets without a prior is proposed. On the basis of estimating the flashing frequency of the blades, a validation function is constructed through spectral analysis to judge the number of blades, and then the rotational frequency is estimated. The blade length is calculated by estimating the maximum Doppler shift. Moreover, the variational mode decomposition (VMD)-based atomic scaling orthogonal matching pursuit (AS-OMP) method is jointly applied to estimate the blade length when suffering from the low PRF and insufficient SNR conditions. Extensive experiments on the simulated and measured data demonstrate that the proposed method outperforms robust micro-motion parameter estimation capability in low PRF and insufficient SNR conditions compared to the traditional time-frequency analysis methods.

The problem of separating multicomponent micro-Doppler (m-D) signals is common in the field of radar signal processing. In some implementations, it is necessary to separate the multicomponent m-D signal that contains missing samples. To address this issue, an optimization model has been developed to recover and decompose multicomponent m-D signals with missing samples. To solve the underlying optimization problem, a two-algorithm-based alternate iteration framework is proposed. This method uses three techniques—the null space property, ridge regression method, and matching pursuit principle—to estimate the individual component, complex-valued differential operator, and regularization parameter. Finally, as shown by both simulation and measured data processing results, the proposed method can accurately separate the multicomponent m-D signal from incomplete data.

The conventional back projection (BP) algorithm is an accurate time-domain algorithm widely used for multiple-input multiple-output (MIMO) radar imaging, owing to its independence of antenna array configuration. The time-delay curve correction back projection (TCC-BP) algorithm greatly reduces the computational complexity of BP but suffers from spatial-variant correction, sidelobe interference and background noise due to the use of coherent superposition of echo time-delay curves. In this article, a residual attention U-Net-based (RAU-Net) MIMO radar imaging method that adapts complex noisy scenarios with spatial variation and sidelobe interference is proposed. On the basis of the U-Net underlying structure, we develop the RAU-Net with two modules: a residual unit with identity mapping and a dual attention module to obtain resolution spatial-variant correction and denoising on real-world MIMO radar images. The network realizes MIMO radar imaging based on the TCC-BP algorithm and substantially reduces the total computational time of the BP algorithm on the basis of improving the imaging resolution and denoising capability. Extensive experiments on the simulated and measured data demonstrate that the proposed method outperforms both the traditional methods and learning-imaging methods in terms of spatial-variant correction, denoising and computational complexity.

Stable and efficient inverse synthetic aperture radar (ISAR) imaging methods for micro-motion targets remain a challenging problem. The approximation accuracy of traditional low-rank constraint solving methods based on nuclear norm is required be improved in high-resolution ISAR imaging and it cannot be applied to large matrices due to the need for singular value decomposition (SVD). In this letter, we replace the nuclear norm constraint with the factor group-sparse regularization (FGSR) without SVD, and propose a micro-motion target ISAR imaging method based on FGSR low-rank representation, which improves the imaging accuracy and has a computational efficiency gain of about 5%. Experiments on simulated and measured data verify the effectiveness of the proposed method.

This paper focuses on the problem of mainlobe interference suppression in radar networks tracking multi‐target scenarios with multiple jammers. A beam allocation method is proposed based on stepwise suppression of mainlobe and sidelobe interference. Although a single radar can effectively suppress sidelobe interference, mainlobe interference necessitates collaborative countermeasures among multiple radar nodes, indicating that the beam allocation method significantly impacts interference suppression performance. We derive the mathematical relationship between target tracking accuracy and beam allocation strategy to address this. Subsequently, we establish a beam allocation model, with the beam allocation matrix as the optimisation variable and target tracking accuracy as the optimisation objective. One intelligent algorithm is employed to solve this model. Simulation results demonstrate that the proposed method optimises the allocation of anti‐jamming resources in the radar network, leading to efficient interference suppression and stable target tracking.

The multiple-input-multiple-output (MIMO) array radar has plentiful application scenarios in the modern intelligent life. The back projection (BP) algorithm is widely used in MIMO array radar imaging for the superior imaging accuracy and generality of measurement configurations. However, the BP algorithm also bring the two-dimensional spatial distortion and background noise. To solve these problems, the U-Net network is introduced in MIMO array radar enhanced imaging in this paper. The experimental results of both random and fixed scatters show that the scatters are accurately reconstructed and positioned, the two-dimensional spatial distortion is corrected and the background noise is eliminated with the proposed method.