In this article, the interference mitigation (IM) problem is tackled as a regression problem. A prior-guided deep learning (DL)-based IM approach is proposed for frequency-modulated continuous-wave (FMCW) radars. Considering the complex-valued nature of radar signals, a complex-valued convolutional neural network, which is different from the conventional real-valued counterparts, is utilized as an architecture for implementation. Meanwhile, as the desired beat signals of FMCW radars and interferences exhibit different distributions in the time–frequency domain, this prior feature is exploited as a regularization term to avoid overfitting of the learned representation. The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN)-based IM approach are verified and analyzed through both simulated and measured radar signals. Compared with the real-valued counterparts, the CV-FCN shows a better IM performance with a potential of half memory reduction in low signal-to-interference-plus-noise ratio (SINR) scenarios. The average SINR of interfered signals has been improved from −9.13 to 10.46 dB. Moreover, the CV-FCN trained using only simulated data can be directly utilized for IM in various measured radar signals and shows a superior generalization capability. Furthermore, by incorporating the prior feature, the CV-FCN trained on only 1/8 of the full data achieves comparable performance as that on the full dataset in low SINR scenarios, and the training procedure converges faster.@en

In recent years, gait-based person identification has gained significant interest for a variety of applications, including security systems and public security forensics. Meanwhile, this task is faced with the challenge of disguised gaits. When a human subject changes what he or she is wearing or carrying, it becomes challenging to reliably identify the subject's identity using gait data. In this paper, we propose an unsupervised domain adaptation (UDA) model, named Guided Subspace Alignment under the Class-Aware condition (G-SAC), to recognize human subjects based on their disguised gait data by fully exploiting the intrinsic information in gait biometrics. To accomplish this, we employ neighbourhood component analysis (NCA) to create an intrinsic feature subspace from which we can obtain similarities between normal and disguised gaits. With the aid of a proposed constraint for adaptive class-Aware alignment, the class-level discriminative feature representation can be learned guided by this subspace. Our experimental results on a measured micro-Doppler radar dataset demonstrate the effectiveness of our approach. The comparison results with several state-of-The-Art methods indicate that our work provides a promising domain adaptation solution for the concerned problem, even in cases where the disguised pattern differs significantly from the normal gaits. Additionally, we extend our approach to more complex multi-Target domain adaptation (MTDA) challenge and video-based gait recognition tasks, the superior results demonstrate that the proposed model has a great deal of potential for tackling increasingly difficult problems.

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In this letter, a radar-based gait authentication method is proposed. We focus on the overfitting problem on the target category caused by limited training data in authentication models and propose a one-class classification model to alleviate this problem. The effectiveness of such model is verified by establishing a radar-based gait dataset, which is composed of gait micro-Doppler spectrograms derived from nine human subjects. The experimental results demonstrate that, under the condition of limited training data, the performances of an authentication model degrade because misclassification of the non-target samples easily occurs. The proposed method effectively avoids this risk, performing the other existing authentication and one-class classification methods on the metric Equal Error Rate.@en