Unlike face recognition, gait recognition does not require the cooperation of human targets, making it an attractive research area in both academia and industry. While there has been extensive research in recent years on the feasibility of using radar micro-Doppler signatures for gait recognition, little research has been conducted on how to deal with unknown target detection in micro-Doppler-based gait recognition problems, referred to as “open-set” gait recognition. In this case, it is probable that the unknown samples will resemble known samples, hence increasing the likelihood of misclassification. In this article, we focus on the problem of multiscenario open-set gait recognition (MSOGR) and present a new open-set classifier that minimizes misclassification in open-set recognition. We create a compact representation space to reduce false negatives (FNs) with a supervised contrastive learning method and propose an ensemble-based out-of-distribution (OOD) detection module so that false positives (FPs) can be alleviated at both the pixel and semantic levels. We perform the radar measurements in eight different walking manners and construct a micro-Doppler-based multiscenario gait dataset to further evaluate the proposed method’s performance. The results reveal that the proposed method outperforms the previous state-of-the-art (SOTA) open-set models, demonstrating its great potential for solving the radar-based MSOGR problem. This research would facilitate gait recognition in complex scenarios and make noncontact sensing applicable to industrial security.

The micromotion of a target will generate a micro-Doppler (m-D) effect in the frequency domain. The m-D effect is regarded as a unique property of the target, which has special significance in target detection, identification, and classification. The classical range-Doppler algorithm cannot obtain a clear inverse synthetic aperture radar (ISAR) image due to the m-D effect induced by micromotion dynamics. The m-D effect induced by periodical micromotion is represented as a sinusoidal modulation in a spectrogram, whereas the Doppler induced by a main body is depicted as the form of a straight line. Therefore, the extraction of an m-D signature is transformed into the separation of a sinusoid and a straight line. The cancellation technique is a classical method for removing ground clutter. Based on the same principle, the cancellation technique is applied to the spectrogram in this letter, which successfully achieves the separation of the m-D signature and gets the clearer ISAR image of the main body. The effectiveness and robustness of the algorithm are proved by simulation results.

Imaging jamming of inverse synthetic aperture radar (ISAR) has been studied extensively in recent decades. Among them, the periodic interrupted sampling repeater jamming (P-ISRJ) technology can generate a series of false targets through matching filtering. However, with the rapid development of anti-jamming technology, it is necessary to improve the existing periodic sampling method to achieve better jamming effects. The latest research proposes that the two-dimensional square jamming strip can be generated based on the nonperiodic interrupted sampling repeater jamming (NP-ISRJ) to cover the target. In this article, the influence of NP-ISRJ on ISAR is studied with the model of escort free-flight. A linear weighted optimization method is proposed to improve the uneven energy distribution. The experimental results show that a square jamming strip with better blanket effects can be generated under the traditional range Doppler imaging method. While for the compressed sensing imaging method, the imaging fails because the sparse condition is no longer satisfied. The validity of the proposed blanket jamming method in different ISAR imaging reconstruction methods is verified.

For space targets with the same shapes and composite relationships in terms of micromotion forms (STSSCRMFs), the accuracy of deep learning approaches with single-domain data is seriously degraded. To address this issue, this article proposes a hybrid neural network based on feature-level fusion that utilizes multidomain radar information to recognize STSSCRMFs. The proposed network simultaneously processes radar cross-section (RCS) time series, high-resolution range profile (HRRP) sequences, and time-frequency (TF) spectrograms through three branches, leveraging their complementary characteristics. Additionally, an attention mechanism is incorporated into the feature-level fusion module to selectively enhance the features of important domains, thereby achieving improved space target recognition. Consequently, the temporal-spatial features of multidomain data are adaptively fused, capturing discriminative and complementary representa-tions, which effectively enhances the accuracy of space target recognition. Experimental results demonstrate the superiority and effectiveness of the proposed method.

Deep learning has been used in inverse synthetic aperture radar (ISAR) imaging to improve resolution performance, but there still exist some problems: the loss of weak scattering points, over-smoothed imaging results, and the universality and generalization. To address these problems, an ISAR resolution enhancement method of exploiting a generative adversarial network (GAN) is proposed in this paper. We adopt a relativistic average discriminator (RaD) to enhance the ability of the network to describe target details. The proposed loss function is composed of feature loss, adversarial loss, and absolute loss. The feature loss is used to get the main characteristics of the target. The adversarial loss ensures that the proposed GAN recovers more target details. The absolute loss is adopted to make the imaging results not over-smoothed. Experiments based on simulated and measured data under different conditions demonstrate that the proposed method has good imaging performance. In addition, the universality and generalization of the proposed GAN are also well verified.

Micro-Doppler is regarded as a unique signature of a target with micro-motions. The sophisticated recognition of the cone-shaped targets can be realized through the micro-Doppler effect. However, it is difficult to extract the micro-motion features perpendicular to the radar line of sight (LOS) effectively. In this paper, a micro-Doppler parameters extraction method of the cone-shaped targets is put forward based on the rotating antenna. First, a new radar configuration is proposed, in which an antenna rotates uniformly on a fixed circle, thus producing Doppler frequency shift. Second, the expression of the micro-Doppler frequency shift induced by the precession cone-shaped target is derived. Then, the micro-Doppler curves of point scatterers at the cone top and bottom are separated by the smoothness of the curves, and the empirical mode decomposition (EMD) method is utilized for the detection and estimation of the coning frequency. Finally, the micro-motion components perpendicular to the radar LOS are inverted by the peak of micro-Doppler frequency curve. Simulation results prove the effectiveness and robustness of the proposed method.