Micro-motion parameters extraction is crucial in recognizing ballistic missiles with a wideband radar. It is known that the phase-derived range (PDR) method can provide a sub-wavelength level accuracy. However, it is sensitive and unstable when the signal-to-noise ratio (SNR) is low. In this paper, an improved PDR method is proposed to reduce the impacts of low SNRs. First, the high range resolution profile (HRRP) is divided into a series of segments so that each segment contains a single scattering point. Then, the peak values of each segment are viewed as non-stationary signals, which are further decomposed into a series of intrinsic mode functions (IMFs) with different energy, using the ensemble empirical mode decomposition with the complementary adaptive noise (EEMDCAN) method. In the EEMDCAN decomposition, positive and negative adaptive noise pairs are added to each IMF layer to effectively eliminate the mode-mixing phenomenon that exists in the original empirical mode decomposition (EMD) method. An energy threshold is designed to select proper IMFs to reconstruct the envelop for high estimation accuracy and low noise effects. Finally, the least-square algorithm is used to do the ambiguous phases unwrapping to obtain the micro-curve, which can be further used to estimate the micro-motion parameters of the warhead. Simulation results show that the proposed method performs well with SNR at −5 dB with an accuracy level of sub-wavelength.

Deep learning stands out as a potent state-of-the-art technique for target recognition. Unfortunately, the trustworthiness and reliability of deep learning networks encounter challenges in radar target recognition. In this letter, an inductive conformal prediction (ICP) enhanced long short-term memory spiking neural network (LSTM-SNN) is proposed. It integrates with the concept of conformal prediction in statistical learning theory and deep learning, and applied to birds and drones recognition with radar. The proposed method can provide good recognition results for drones and birds with supplying confidence and credibility for each identification, and yields a confidence interval containing the true value of the estimated at the desired confidence level, such as 98%. The benefits of the LSTM-SNN method were demonstrated with the bird detection datasets obtained by radar in the airport.

In the domain of micromotion target recognition, target characteristics can be extracted through either broadband or narrowband radar echoes. However, due to technical limitations and cost constraints in acquiring broadband radar waveform data, recognition can often only be performed through narrowband radar waveforms. To fully utilize the information embedded within narrowband radar waveforms, it is necessary to conduct in-depth research on multi-dimensional features of micromotion targets, including radar cross-sections (RCSs), time frequency (TF) images, and cadence velocity diagrams (CVDs). To address the limitations of existing identification methodologies in achieving accurate recognition with narrowband echoes, this paper proposes a graph fusion network based on a cross-modal attention mechanism, termed GF-AM Net. The network first adopts convolutional neural networks (CNNs) to extract unimodal features from RCSs, TF images, and CVDs independently. Subsequently, a cross-modal attention mechanism integrates these extracted features into a graph structure, achieving multi-level interactions among unimodal, bimodal, and trimodal features. Finally, the fused features are input into a classification module to accomplish narrowband radar micromotion target identification. Experimental results demonstrate that the proposed methodology successfully captures potential correlations between modal features by incorporating cross-modal multi-level information interactions across different processing stages, exhibiting exceptional accuracy and robustness in narrowband radar micromotion target identification tasks.

Low-frequency ultra-wideband synthetic aperture radar (UWB SAR) has the high-resolution imaging and foliage-penetrating ability, which can detect the foliage-concealed target. However, due to the jungle detection environment and the low-frequency UWB SAR characteristics, there are often some non-target strong scattering points in the low-frequency UWB SAR images, which may increase the difficulty of the foliage-concealed target change detection. To improve the change detection rate of the foliage-concealed target, a foliage-concealed target change detection strategy based on the image stacks in the low-frequency UWB SAR images is proposed. In the image preprocessing, a relative radiometric correction method based on the bidirectional linear regression model is presented, which can eliminate the low-frequency UWB SAR image changes caused by the non-target factors. Besides, in the change detection processing, the multiple difference images are first obtained by subtracting the image to be detected with the multiple reference images, then the Gaussian probability density function is used to model the distribution of the amplitude of these difference images, finally the generalized likelihood ratio test is used for the target change detection, which effectively suppresses the interfere such as the tree trunk clutter. Experimental results tested on the CARABAS-II SAR dataset demonstrate the correctness and effectiveness of the proposed strategy, which can improve the change detection probability of the foliage-concealed target with the lower false alarm rate.

The escalating threat posed by low-altitude swarm targets underscores the critical need for precise tracking and situation awareness to secure key areas. While existing tracking methods based on random matrix theory offer promising opportunities, they face significant challenges. The high similarity among swarm targets, combined with radar resolution limitations, often leads to instabilities in target counts and measurements due to occlusion, environmental factors, and other disturbances, significantly increasing tracking complexity. To address these challenges, we design a digital staring radar system integrated with an adaptive random matrix method for efficient tracking of low-altitude swarm targets. The system achieves full spatiotemporal coverage without beam scanning or complex resource scheduling, enabling simultaneous detection and tracking of multiple targets. Algorithmically, the random matrix model is enhanced by introducing extension parameters to accurately capture the dynamic changes in swarm shape. Leveraging an adaptive Rao-Blackwellized Particle Filter (RBPF), the presented method jointly estimates the motion and extension states of swarm targets. Extensive simulation experiments and real-data validation demonstrate that the proposed method significantly improves the estimation accuracy for swarm extension states under complex shape variations while maintaining high precision in motion state estimation. This work provides a practical and effective solution for countering low-altitude swarm threats, with strong potential for real-world security applications.

The low-frequency ultra-wideband synthetic aperture radar (UWB SAR) has the ability of the foliage-penetrating and high-resolution imaging, which can detect the foliage-concealed target. However, due to low-frequency UWB SAR characteristics and forest detection environments, there are usually some non-target strong scattering points in the low-frequency UWB SAR images, which may increase the difficulty of the foliage-concealed target change detection. To solve the problem of the weak anti-jamming ability of the foliage-concealed target change detection, a foliage-concealed target change detection scheme based on the convolutional neural network (CNN) in the low-frequency UWB SAR images is proposed, which combines the traditional image difference method and deep learning method. Firstly, a relatively low inspection threshold is set for the target change detection based on the image difference method, which can obtain a lot of the position information of the detection point. Moreover, for the target characteristics in the foliage-concealed scenarios, the corresponding data extraction and enhancement techniques are used to effectively extract the detection point samples from the detection image and reference image, which can prevent the overfitting of the model training caused by the sample scarcity. Finally, the samples of the detection points are input to the target classification network with the double input and single output for the classification training and testing. Experimental results tested on the CARABAS-II SAR dataset demonstrate the correctness and effectiveness of the proposed scheme, which has the better change detection performance and anti-interference capability.