An Antijamming Method Based on Multichannel Singular Spectrum Analysis and Affinity Propagation for UWB Ranging Sensors

Wang, Xun; Zhang, Shuning; Chen, Si; Linsheng,; Hou,; Zhu, Lingzhi

doi:10.1109/jsen.2023.3266231

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…UWB sensors have high-resolution characteristics [13], and the echo in the onedimensional time domain is similar to those of the HRRP (high-resolution range profile). Many researchers have launched studies on HRRP target recognition using machine learning methods, such as Extreme Value Distribution [14], continuous learning based on CVAE-GAN [15], methods for CNN with Multi-Axis Attention and Residual Connections [16], a temporal-spatial feature aggregation network (TSFA-Net) [17], Composite Deep Networks [18], and Attention-ERTCN [19].…”

Section: Introductionmentioning

confidence: 99%

Carrier-Free Ultra-Wideband Sensor Target Recognition in the Jungle Environment

Li,

Zhang,

Zhu

et al. 2024

Remote Sensing

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Carrier-free ultra-wideband sensors have high penetrability anti-jamming solid ability, which is not easily affected by the external environment, such as weather. Also, it has good performance in the complex jungle environment. In this paper, we propose a jungle vehicle identification system based on a carrier-free ultra-wideband sensor. Firstly, a composite jungle environment with the target vehicle is modeled. From this model, the simulation obtains time-domain echoes under the excitation of carrier-free ultra-wideband sensor signals in different orientations. Secondly, the time-domain signals are transformed into MTF images through the Markov transfer field to show the statistical characteristics of the time-domain echoes. At the same time, we propose an improved RepVGG network. The structure of the RepVGG network contains five stages, which consist of several RepVGG Blocks. Each RepVGG Block is created by combining convolutional kernels of different sizes using a weighted sum. We add the self-attention module to the output of stage 0 to improve the ability to extract the features of the MTF map and better capture the complex relationship between characteristics during training. In addition, a self-attention module is added before the linear layer classification output in stage 4 to improve the classification accuracy of the network. Moreover, a combined cross-entropy loss and sparsity penalty loss function helps enhance the performance and accuracy of the network. The experimental results show that the system can recognize jungle vehicle targets well.

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Section: Introductionmentioning

confidence: 99%

Carrier-Free Ultra-Wideband Sensor Target Recognition in the Jungle Environment

Li,

Zhang,

Zhu

et al. 2024

Remote Sensing

Self Cite

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“…The work [48] describes the method's applications in order to suppress noise in the signals coming from the antenna. Similarly, the effectiveness of SSA is reflected in the study [49] in filtering electromagnetic noise that appears in radio-electronic systems and is one of the most active types of interference. An eye blink detection method is proposed in [50], which can be used in driver fatigue systems as well as other applications.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Maintenance Scenarios for Equipment and Control Systems in Industry

Koteleva,

Valnev

2023

Applied Sciences

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The well-known methods of scene extraction on video are focused on analyzing the similarity between frames. However, they do not all analyze the composition of the image scene, which may remain the same during maintenance. Therefore, this paper proposes an algorithm for equipment maintenance scene detection based on human hand tracking. It is based on the assumption that, when servicing technological equipment, it is possible to determine the change in repair action by the position of the service engineer’s hands. Thus, certain information and the algorithm that processes these changes allow us to segment the video into actions performed during the service. We process the time series obtained by moving the hand position using spectral singular value decomposition for multivariate time series. To verify the algorithm, we performed maintenance on the control cabinet of a mining conveyor and recorded the work on a first-person video, which was processed using the developed method. As a result, we obtained some scenes corresponding to opening the control cabinet, de-energizing the unit, and checking the contacts with a multimeter buzzer test. A third-person video of motor service was similarly processed. The algorithm demonstrated the results in separate scenes of removing screws, working with a multimeter, and disconnecting and replacing motor parts.

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