Radar-based human activity recognition (HAR) provides a non-contact method for many scenarios, such as human–computer interaction, smart security, and advanced surveillance with privacy protection. Feeding radar-preprocessed micro-Doppler signals into a deep learning (DL) network is a promising approach for HAR. Conventional DL algorithms can achieve high performance in terms of accuracy, but the complex network structure causes difficulty for their real-time embedded application. In this study, an efficient network with an attention mechanism is proposed. This network decouples the Doppler and temporal features of radar preprocessed signals according to the feature representation of human activity in the time–frequency domain. The Doppler feature representation is obtained in sequence using the one-dimensional convolutional neural network (1D CNN) following the sliding window. Then, HAR is realized by inputting the Doppler features into the attention-mechanism-based long short-term memory (LSTM) as a time sequence. Moreover, the activity features are effectively enhanced using the averaged cancellation method, which improves the clutter suppression effect under the micro-motion conditions. Compared with the traditional moving target indicator (MTI), the recognition accuracy is improved by about 3.7%. Experiments based on two human activity datasets confirm the superiority of our method compared to traditional methods in terms of expressiveness and computational efficiency. Specifically, our method achieves an accuracy close to 96.9% on both datasets and has a more lightweight network structure compared to algorithms with similar recognition accuracy. The method proposed in this article has great potential for real-time embedded applications of HAR.

The interrupted sampling repeater jamming (ISRJ) is considered an efficient deception method of jamming for coherent radar detection. However, current countermeasure methods against ISRJ interference may fail in detecting weak echoes, particularly when the transmitting power of the jammer is relatively high. In this paper, we propose a novel countermeasure scheme against ISRJ based on Bayesian compress sensing (BCS), where stable target signal can be reconstructed over a relatively large range of signal-to-noise ratio (SNR) for both single target and multi-target scenarios. By deriving the ISRJ jamming strategy, only the unjammed discontinuous time segments are extracted to build a sparse target model for the reconstruction algorithm. An efficient alternate iteration is applied to optimize and solve the maximum a posteriori estimate (MAP) of the sparse targets model. Simulation results demonstrate the robustness of the proposed scheme with low SNR or large jammer ratio. Moreover, when compared with traditional FFT or greedy sparsity adaptive matching pursuit algorithm (SAMP), the proposed algorithm significantly improves on the aspects of both the grating lobe level and target detection/false detection probability.

With the continuous advancement of automotive intelligence and ubiquitous connection, In-Vehicle Networks (IVN) are confronted with heightened security challenges. Attackers can inject false messages to manipulate or disrupt critical functional modules on the vehicle, leading to significant safety issues. Given the real-time requirements and resource constraints of the in-vehicle network, a lightweight Intrusion Detection System (IDS) is needed to deal with the security threats caused by attacks. This paper proposes an effective intrusion detection method to address these requirements. The method combines refined CAN traffic features with a lightweight Deep Learning (DL) network. The time interval series, ID and the CAN message payload are extracted by a T-shaped window, vectorized to form a one-dimensional data frame, and the detection is completed by a lightweight 1D deep learning model using efficient convolution calculation. In binary and multiclass experiments on two publicly available datasets, the feature refinement strategy is discussed. Compared to existing deep learning-based intrusion detection methods, our approach demonstrates advantages in both intrusion detection performance and computational complexity, highlighting its deployment potential in embedded IVN environments.

The Industrial Internet of Things (IIoT) is recognized as one of the revolutionary technologies driving smart manufacturing and improving productivity. As manufacturing processes grow increasingly intricate, the entire manufacturing ecosystem encompasses multiple managed IoT domains. Within this highly interconnected environment, devices from diverse domains must collaborate, leading to considerable apprehensions about the security and privacy of device-to-device communications. Current authentication methods encounter several challenges. Traditional authentication schemes overly rely on trusted third parties, rendering them susceptible to external attacks or internal spoofing. This susceptibility gives rise to a range of security and privacy concerns. In response to these challenges, this paper aims to contribute to a more secure and efficient service scheme for smart factories by devising a blockchain-based distributed IoT architecture. The proposed scheme introduces a federated blockchain to establish trust among different domains, thereby enabling secure connections between devices in distinct domains. Through security analysis, it is proved that the proposed authentication scheme has integrity, mutual authentication, scalability, and resistance to four attacks. Furthermore, efficiency analysis experiments show that our scheme is feasible for smart factories, and as the number of peer nodes increases, the performance and efficiency of the blockchain network become better.

Range ambiguity resolving and deceptive jamming suppression are both challenges for high-resolution wide-swath (HRWS) SAR imagery. With the accommodation of the digital beamforming (DBF) techniques, agile waveforms are of potential to tackle the ambiguity-resolving and anti-jamming tasks simultaneously. In this paper, an algorithm blending agile waveforms and elevation DBF is proposed for wide-swath SAR imagery and adaptive jamming suppression. A shift-stepped Costas DFC (SSC-DFC) waveform is developed with optimal shifted orthogonality. Compared with the orthogonal waveform with smeared ambiguous energy, SSC-DFC can provide precise ambiguity energy position for the spatial filter construction. Meanwhile, cyclic shifts coherence of the agile waveforms minimizes the azimuth ambiguity residual after the Doppler filtering. A serial DBF is designed for adaptive false target extraction and jammer location estimation. The jammer direction notch is only added in the false target area while executing the wide image deblurring. The sub-region DBF avoids the dark strip in the final wide-swath image effectively. Extensive simulation has been presented to verify the practicability of the proposed scheme.

Conventional sparsity-driven synthetic aperture radar (SAR) imagery proceeds via $\ell _{1}$ regularization, or named by least absolute shrinkage and selection operator (LASSO). However, followed by the enhanced sparse feature, structures of the scenes or targets of interest in weak scattering are easily lost. Therefore, it becomes difficult to make use of the high-resolution SAR data, and even high costs have been paid for the resolution. In this article, a novel structure-guaranteed SAR (SG-SAR) imaging algorithm is proposed by utilizing the morphology metric for the cluster feature of the scatterers of the scenes/targets of interest. By introducing structural priors in terms of morphology norm, the intended structure features can be highlighted via convex regularization. More specifically, to accommodate complicated scenarios or targets, the structure element (SE) in the morphology regularizer is designed to be spatially variant under structure tensor representation. Different from conventional convex optimizations, the proposed SG-SAR algorithm is solved under the alternating direction method of multipliers (ADMMs), which is flexible to incorporate with the super-resolution imagery. In such cases, both sparse and structural features can be simultaneously enhanced, even with limited measurements and in a low signal-to-noise ratio (SNR). Superior convergence and robustness can be guaranteed. Moreover, a grouping mask scheme is used to accommodate the complex-valued SAR data. Finally, both simulated and measured SAR data are applied for the validation. Comparisons with the conventions are performed in terms of phase transition analysis, so as to verify the superiority of the proposed algorithm both qualitatively and quantitatively.