Impulse radio ultra-wideband (IR-UWB) radar has the advantages of low cost, high resolution, and independence of light and weather conditions. Its potential in human activity recognition (HAR) for IoT device sensing draws interest. One challenge in this domain is effectively representing spatial static and temporal dynamic information in echo sequences. Transformers, used extensively in NLP and CV, have powerful sequence long-range dependency modeling capabilities. However, in the field of radar HAR, the application research of Transformers is still insufficient. In addition, there is currently a lack of publicly available IR-UWB radar human action datasets. To this end, we proposed various fine-grained feature image calculation methods and designed an IR-UWB Radar Human Activity dataset (IURHA2023). This paper presents a Swin-Transformer encoder combining cosine similarity attention and patch overlap to obtain deep spatio-temporal features of human action feature images. Compared with other proposed Transformer models or traditional CNNs and RNNs, the improved Swin-Transformer encoder performs better. To further improve the feature learning capability of the backbone network and the robustness to echo variations, we propose a supervised contrastive learning-enhanced Swin-Transformer (SCL-SwinT). It obtains distinctions and compact embeddings by comparing the similarities of positive and negative examples partitioned according to labels. Experimental results on the IURHA2023 dataset show that SCL-SwinT achieves a recognition rate exceeding 90%, and the inference speed on IoT edge devices satisfies real-time applications. Ablation experiments demonstrate the effectiveness of the proposed components. In addition, SCL-SwinT exhibits good robustness to environmental factors like noise, multipath, and distance.

The satisfaction of various basic requirements of cognitive radar by Ultra-wideband (UWB) signals makes UWB cognitive radar attract extensive attention. The variety and large dynamic range of jamming in the UWB spectrum range make jamming identification critical and challenging. However, the traditional method has low recognition accuracy, high computational complexity, and difficulty in multi-signal recognition. In this paper, we propose a multi-scale attention network (MANet) for carrier-free UWB cognitive radar to identify target signals and nine types of jamming signals. MANet extracts different fine features by multi-scale dilation convolution. The features are stitched together in the channel dimension. The subtle features that are beneficial for recognition are then substantially enhanced using channel attention blocks. The proposed method combines the time domain and frequency domain features to improve the recognition performance by using the powerful feature extraction ability and generalization ability of MANet. Simulation results show that the overall recognition accuracy of the method is 93.1%, with less storage space, shorter FLOPs and inference time than the five recognition methods, and better and more stable recognition performance is also achieved at low jamming-to-noise ratios (JNR).

Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.

In this study, we introduce MobileViTX, an enhanced MobileViT architecture for human activity recognition in impulse radio ultra-wideband (IR-UWB) radar applications. MobileViT is a lightweight Vision Transformer mainly consisting of MobileViT blocks and MobileNetv2 blocks. Modifications to the MobileNetv2 block include adding a Drop Path and an SE module and altering activation functions to hard-sigmoid and hard-swish. Additionally, the self-attention in the MobileViT block is transformed to possess linear complexity. These adjustments aim to accelerate inference while preserving high accuracy. We experiment with a dataset from 20 individuals performing 20 distinct actions, using 5-fold cross-validation to assess our model’s performance. Results show MobileViTX outperforms the original MobileViT and other models in both recognition rate and efficiency.

For forward-looking linear-array synthetic aperture radar (FLLA-SAR) with constant acceleration, the conventional hyperbolic range model is incorrect, and the range-dependent range cell migration (RCM) cannot be ignored any more. Hence, the traditional chirp scaling (CS) algorithm based on the hyperbolic range model also cannot be used. To overcome these problems, a modified range model for the FLLA-SAR which has taken the platform's acceleration into consideration is proposed in this letter. Based on the model, an improved CS method is proposed for FLLA-SAR to eliminate the space-variant characteristic of the RCM. Simulation results are presented to validate the range model and the proposed method.

Synthetic aperture radar (SAR) may be attacked by multifarious kinds of the active-jamming, which will lead to the ineffectiveness of SAR in complex electromagnetic environment. In this paper, a novel multi-electromagnetic jamming counter-measure for the airborne SAR is proposed based on maximum signal-to-noise ratio (SNR) blind source separation. Firstly, the imaging geometry and multi-component mixed signal model of airborne SAR are established. Then, based on multi-component mixed signal matrix, a blind source separation (BSS) method based on the maximum SNR is proposed to separate the real target echo signal from the multi-electromagnetic jamming signals. After real target echo signal identification, the high resolution images of the interested target area can be achieved by the corresponding SAR imaging method. The simulated and measured data results are present to prove the feasibility and effectiveness of the proposed method.