Enhanced Near-Field Interference Suppression Scheme for the Non-Cooperative Underwater Acoustic Pulse Detection of the Towed Linear Array

Wei, Kun; Fang, Shiliang; Tao, Junwu

doi:10.3390/jmse10020250

Cited by 1 publication

(1 citation statement)

References 14 publications

(27 reference statements)

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“…Many scholars have been working on problems related to electromagnetic interference, scattering, and multipath effects in recent years. The article [26] investigates the problem of electromagnetic solid interference suppression without a priori information in noncooperative scenarios, the issue of near-field EMI suppression is investigated, and the proposed algorithm effectively suppresses strong EMI without a priori knowledge while effectively capturing the target signal. In the article [27], the authors proposed a method to effectively solve the problem of near-area electromagnetic scattering of scatterers under external field irradiation.…”

Section: Gps Pcsmentioning

confidence: 99%

An Excess Kurtosis People Counting System Based on 1DCNN-LSTM Using Impulse Radio Ultra-Wide Band Radar Signals

Zhang,

Dang,

Hao

2023

Electronics

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As the Artificial Intelligence of Things (AIOT) and ubiquitous sensing technologies have been leaping forward, numerous scholars have placed a greater focus on the use of Impulse Radio Ultra-Wide Band (IR-UWB) radar signals for Region of Interest (ROI) population estimation. To address the problem concerning the fact that existing algorithms or models cannot accurately detect the number of people counted in ROI from low signal-to-noise ratio (SNR) received signals, an effective 1DCNN-LSTM model was proposed in this study to accurately detect the number of targets even in low-SNR environments with considerable people. First, human-induced excess kurtosis was detected by setting a threshold using the optimized CLEAN algorithm. Next, the preprocessed IR-UWB radar signal pulses were bundled into frames, and the resulting peaks were grouped to develop feature vectors. Subsequently, the sample set was trained based on the 1DCNN-LSTM algorithm neural network structure. In this study, the IR-UWB radar signal data were acquired from different real environments with different numbers of subjects (0–10). As indicated by the experimental results, the average accuracy of the proposed 1DCNN-LSTM model for the recognition of people counting reached 86.66% at ROI. In general, a high-accuracy, low-complexity, and high-robustness solution in IR-UWB radar people counting was presented in this study.

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Section: Gps Pcsmentioning

confidence: 99%