The high-precision separation of micro-Doppler curves is the key to micro-motion feature extraction and parameter estimation for ballistic target in midcourse. The micro-Doppler curves of each scatter overlap seriously in the time-frequency domain and are also affected by nonideal scattering phenomena such as strong noise and occlusion effects, which poses a significant challenge to the traditional curve separation methods. Aiming at this problem, a micro-Doppler curve separation algorithm under nonideal scattering conditions is proposed in this paper. First, the micro-Doppler curve and noise are separated through the U-Net model in the time-frequency domain. Then on the basis of eliminating the effect of redundant and pseudo plots by plot condensation and plot processing, the micro-Doppler plots are associated and regrouped based on interpolation and curve smoothness function. Finally, the effectiveness and robustness of the proposed algorithm have been illustrated by extensive simulation experiments.

It is of great significance to detect drones in airspace due to the substantial increase and regrettable misuse in the consumer market. In this paper, we establish a micro-motion theoretical model of a drone and analyze the micro-Doppler signature of rotor targets and the flicker mechanisms of the multi-rotor targets. Hence, for the target recognition problem of multi-rotor drones, a multi-rotor target micro-Doppler parameter estimation method is proposed. Firstly, a signal frequency domain segmentation method is proposed based on the complex variational mode decomposition (CVMD) to separate the high-frequency part of the high-frequency flicker in the frequency domain. Secondly, for the signal after frequency domain segmentation, a flicker time domain position method based on singular value decomposition (SVD) is proposed. Finally, by integrating CVMD frequency domain segmentation and SVD time domain positioning, the reconstruction of multi-rotor target scintillation at different speeds is realized, and the micro-motion parameters of rotor blades are successfully estimated. The simulation results show that the method has high accuracy in estimating the micro-motion parameters of a multi-rotor, which makes up for the shortage of the traditional method in estimating the micro-motion parameters of the multi-rotor target.

The micro-movement feature is recognized as one of the practical features of space target recognition in academic circles. The separation of the micro-Doppler curve of the scattering center is the key to feature extraction and parameter estimation, which depends on the time–frequency analysis method. The existing techniques have low separation accuracy and adaptability when there are overlap and noise in the time–frequency domain. This paper proposes a micro-Doppler feature extraction algorithm of a space target based on the modified synchro-reassigning transform (MSRT) and ridge segment linking. The MSRT can eliminate repeated assignment problems, has more accurate micro-Doppler frequency estimates than the synchro-reassigning transform, and has lower computational complexity than second-order synchronous compression and synchronous extraction transforms. The re-linking of the ridge realizes the correct connection of the micro-Doppler curves of each scattering center. The simulation data and the electromagnetic calculation data verify the method’s effectiveness.

Extracting micro-Doppler (m-D) curves from timefrequency distributions (TFD) often encounters difficulties due to signal overlap, component discontinuity, and high noise levels. These challenges limit the effectiveness of such extractions in accurately representing the underlying signal characteristics. Addressing these limitations, this paper introduces a deep learning approach — the Recurrent Self-Attention Separation Network (RSS Net). The network separates independent m-D curves by dividing the TFD of multi-component signals into distinct masks for each signal component. These masks can be integrated with traditional methods or directly used for such estimations. We have compared and analyzed the performance of both approaches under various conditions. The network's effectiveness has been validated through extensive testing on the m-D curves of spatial conical targets in complex scenarios. Our experimental results emphatically demonstrate the network's superior performance over existing methodologies.

Infrared (IR) small target detection faces two major challenges: single-frame-based methods are highly sensitive to random noise, while multi-frame-based approaches often neglect the dynamic nature of background scenes, resulting in high false alarm rates. To address these issues, this paper proposes a novel ring local contrast measure weighted by spatial–temporal prior (WSTRLCM). WSTRLCM comprises two key modules: a spatial-temporally enhanced ring local contrast and a spatial-temporal weighting factor. Specifically, a spatial–temporal enhancement scheme is first applied to the original image sequence to boost target saliency, and the ring contrast computation is optimized for improved efficiency. For the weighting factor, a small target enhancement strategy based on image block gradients is introduced. By incorporating structure tensor analysis, effective back-ground suppression is achieved, resulting in a refined spatial-temporal enhancement sequence. Furthermore, a tailored multi-frame difference approach, designed according to target motion characteristics, is employed to generate the final weighting factor. The Hadamard product of the local contrast and the weighting factor is then computed, and adaptive thresholding is applied to obtain the final detection map. Experimental results on public datasets demonstrate that WSTRLCM achieves superior performance compared to several state-of-the-art methods, exhibiting enhanced target detection capability, strong background suppression. The code is available at https://github.com/biangbiangliang/WSTRLCM.

The high-speed movement of a ballistic target will cause folding and translation of the micro-Doppler, which will affect the extraction of micro-motion features. To address the adverse effects of high-speed movement of ballistic targets in midcourse on the extraction of micro-motion features, a novel translational compensation algorithm based on template matching is proposed. Firstly, a 512 × 512 time-frequency map is obtained by binarization and down-sampling. The matching template then convolves the time-frequency map to obtain contour-like points. Then, the upper and lower contour points are preliminarily determined by the extreme value, and all actual contour points are screened out through structural similarity. Lastly, the upper and lower trend lines are determined and translation parameters for compensation by polynomial fitting are estimated. Simulation results show that the proposed algorithm has lower requirements for time-frequency resolution, higher precision and lower time complexity as a whole. Furthermore, it is also applicable to spectral aliasing.