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2021 CIE International Conference on Radar, Radar 2021

作者： Peng, Cong Chai, Lei Yi, Wei Chen, Linxiu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781665498142

This paper addresses the problem of maneuvering multi-target tracking by a network of sensors having different and limited fields of view (FoV s). Each local sensor runs the Gaussian Mixture Probability Hypothetical Density (GMPHD) filter. Due to the target maneuver, based on a single motion model, severe tracking performance degradation can be observed. We propose to use multi-model (MM) method to realize the adaptation to motion characteristic so as to overcome maneuverability of targets. Then considering FoV s of sensors in the network are different and limited, the standard weighted arithmetic average (WAA) fusion is no longer applicable and leads to an underestimation of the target number. Therefore, we use state-dependent WAA (SD-WAA) fusion rule, which performs the WAA in a more robust way by calculating a set of state-related fusion weights. Numerical experiment is designed to demonstrate the efficacy of the proposed method. © 2021 IEEE.

关键词： fields of view gaussian mixture maneuvering multi-target tracking multi-model method probability hypothesis density filter state-dependent weighted arithmetic average

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Act Now: A Novel Online Forecasting Framework for Large-Scale Streaming Data

arXiv

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arXiv 2024年

作者： Liang, Daojun Zhang, Haixia Wang, Jing Yuan, Dongfeng Zhang, Minggao School of Information Science and Engineering Shandong University China School of Control Science and Engineering Shandong University China Ocean College Jiangsu University of Science and Technology China School of Qilu Transportation Shandong University China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration China

In this paper, we find that existing online forecasting methods have the following issues: 1) They do not consider the update frequency of streaming data and directly use labels (future signals) to update the model, leading to information leakage. 2) Eliminating information leakage can exacerbate concept drift and online parameter updates can damage prediction accuracy. 3) Leaving out a validation set cuts off the model’s continued learning. 4) Existing GPU devices cannot support online learning of large-scale streaming data. To address the above issues, we propose a novel online learning framework, Act-Now, to improve the online prediction on large-scale streaming data. Firstly, we introduce a Random Subgraph Sampling (RSS) algorithm designed to enable efficient model training. Then, we design a Fast Stream Buffer (FSB) and a Slow Stream Buffer (SSB) to update the model online. FSB updates the model immediately with the consistent pseudo- and partial labels to avoid information leakage. SSB updates the model in parallel using complete labels from earlier times. Further, to address concept drift, we propose a Label Decomposition model (Lade) with statistical and normalization flows. Lade forecasts both the statistical variations and the normalized future values of the data, integrating them through a combiner to produce the final predictions. Finally, we propose to perform online updates on the validation set to ensure the consistency of model learning on streaming data. Extensive experiments demonstrate that the proposed Act-Now framework performs well on large-scale streaming data, with an average 28.4% and 19.5% performance improvement, respectively. Experiments can be reproduced via https://***/Anoise/Act-Now. © 2024, CC BY.

关键词： Spatio-temporal data

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AI-aided power electronic converters automatic online real-time efficiency optimization method

引用

Fundamental Research 2023年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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Interpretable DRL-based Maneuver Decision of UCAV Dogfight

arXiv

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arXiv 2024年

作者： Han, Haoran Cheng, Jian Lv, Maolong School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Air Traffic Control and Navigation College Air Force Engineering University Xi’an710051 China College of Aeronautics Engineering Air Force Engineering University Xi’an710038 China

This paper proposes a three-layer unmanned combat aerial vehicle (UCAV) dogfight frame where Deep reinforcement learning (DRL) is responsible for high-level maneuver decision. A four-channel low-level control law is firstly constructed, followed by a library containing eight basic flight maneuvers (BFMs). Double deep Q network (DDQN) is applied for BFM selection in UCAV dogfight, where the opponent strategy during the training process is constructed with DT. Our simulation result shows that, the agent can achieve a win rate of 85.75% against the DT strategy, and positive results when facing various unseen opponents. Based on the proposed frame, interpretability of the DRL-based dogfight is significantly improved. The agent performs yo-yo to adjust its turn rate and gain higher maneuverability. Emergence of"Dive and Chase" behavior also indicates the agent can generate a novel tactic that utilizes the drawback of its opponent. © 2024, CC0.

关键词： Reinforcement learning

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Transformer based Online Continuous Multi-Target Tracking with State Regression

Transformer based Online Continuous Multi-Target Tracking wi...

引用

International Conference on control, Automation and Information Sciences (ICCAIS)

作者： Xinwei Wei Linxiu Chen Chenyu Zhang Yiru Lin Linao Zhang Xiaokai Liu Jixiang Jiang Wei Yi School of Information and Communication Engineering University of Electronic Science and Technology of China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Research Institute of Electronic Science and Technology University of Electronic Science and Technology of China

Multi-target Tracking (MTT) is the process of processing received measurements to maintain estimates of the current status of multiple targets, with important applications to autonomous driving, aerial reconnaissance, underwater operations, and others. In the model-based setting, Bayesian filtering can provide the theoretical optimal estimate in a single target scenario. However, in complex situations, uncertain factors such as changes in the number of targets will cause the amount of calculation to increase exponentially, resulting in a decline in tracking accuracy. To solve that problem, model-free methods based on deep-learning provide an attractive alternative, especially the state-of-the-art architecture Transformer based encoder-decoder prediction model, which outperforms the Bayesian filters in the single frame prediction tasks. However, when switching to continuous tracking, these algorithms need to be trained separately frame by frame to adapt to the new tasks. Still, there is no correlation between their predictions from different frames, which prevents them from fully utilizing all the measurements. In this paper, we propose an end-to-end Transformer based MTT method with state autoregression, which allows the model to have the capability of online continuous tracking and make total use of the entire trajectory. The results show that the proposed model is a great extension from single-frame prediction to online continuous tracking.

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A Novel Microwave Humidity Sensor Based on a Complementary Interdigital Capacitor Resonator

SSRN

引用

SSRN 2024年

作者： Wang, Litian Chen, Jie Li, Dan Qian, Lirong Xiong, Yang Miao, Lin Zhang, Rong Li, Cuiping Tianjin Key Laboratory of Film Electronic and Communication Devices School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Resonant for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China

This study presents a novel microwave humidity sensor that utilizes a complementary interdigital capacitor resonator (CIDCR), an advanced design derived from the traditional complementary split ring resonator (CSRR) structure. The CIDCR design enhances the sensor's performance by concentrating the electric field in a more compact region, leading to improved sensitivity and miniaturization. The sensor is coated with a composite film of chitosan (CS) and polyacrylamide-glycerol (PAMG), combining the high moisture sensitivity of PAMG with the stability of CS. The CIDCR-based sensor demonstrates superior performance, including high sensitivity, a broad detection range, rapid response/recovery times, and excellent long-term stability. Density functional theory (DFT) calculations further elucidate the moisture-sensitive mechanisms at the molecular level, highlighting the role of hydrogen bonding in the composite film. Application tests confirm the sensor's potential for use in respiratory monitoring, non-contact sensing, and health monitoring, showcasing the advantages of the CIDCR structure in achieving enhanced humidity sensing capabilities. © 2024, The Authors. All rights reserved.

关键词： Humidity sensors

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Modulation recognition of composite signal based on ResNet and frequency domain graph 3

Modulation recognition of composite signal based on ResNet a...

引用

2021 3rd International Conference on Artificial Intelligence and Computer Science, AICS 2021

作者： Wang, Lijun Han, Yu Ge, Hongfang Zhao, Yongkuan Shen, Lei Science and Technology on Communication Information Security Control Laboratory Jiaxing China The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China School of Communication Engineering Hangzhou Dianzi University Hangzhou China

Aiming at the problem of composite signal recognition in measurement and control system, a modulation recognition method of composite signal is proposed based on clipped internal modulation signal frequency image and residual network. Residual network has the advantages of fast convergence and high recognition accuracy, which is used to realize the recognition of different modulation modes and different bandwidth signals. The experimental results show that, compared with the traditional decision tree algorithm and the constellation based CNN recognition algorithm, the recognition performance of the proposed method is improved by more than 3dB for the variable bandwidth composite modulation signal set (BPSK-FM and QPSK-FM with internal modulation bandwidth of 128kHz, BPSK-FM and QPSK-FM with internal modulation bandwidth of 2kHz and 2FSK-FM with internal modulation bandwidth of 32kHz and 2kHz). © Journal of Physics: Conference Series 2021.

关键词： Quadrature phase shift keying

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Modulation recognition of composite modulation signal based on two-fold digital receiver and goodness of fit test

Modulation recognition of composite modulation signal based ...

引用

2021 IEEE International Conference on Electronic technology, communication and Information, ICETCI 2021

作者： Lijun, Wang Yu, Han Pan, Zhou Hongfang, Ge Lei, Shen Science and Technology on Communication Information Security Control Laboratory Jiaxing China The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China School of Communication Engineering Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781665432511

Aiming at the modulation recognition problem of mixed signal set {BPSK, QPSK, BPSK-FM, QPSK-FM} with unknown parameters, this paper proposes a modulation recognition algorithm based on two-fold PLL (phase locked loop) and goodness of fit test. The PLL is used to extract the features of the composite modulation signal, and the goodness of fit test is used to measure the distance of the phase cumulative distribution of the demodulation information. Theoretical analysis and simulation results show that the performance of the recognition method based on two-fold PLL and goodness of fit test is better than the widely used modulation recognition method based on constellation. ©2021 IEEE.

关键词： Phase locked loops

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An Electromagnetic Signal Classification Method Inspired by the Visual Characteristics of Biological Dual-Channel

SSRN

引用

SSRN 2022年

作者： Dai, Shaogang Zhou, Huaji Science and Technology on Communication Information Security Control Laboratory Jiaxing314000 China School of communication engineering Hangzhou Dianzi University Hangzhou310018 China

Automatic modulation classification of electromagnetic signals has an important role in the field of civil and military signal processing, for which research is mainly based on deep learning methods. However, existing deep learning models generally suffer from problems such as network redundancy and non-interpretability. Since the biological visual system has extraordinary visual signal recognition ability, and the system has been widely studied at the micro (neuron), mesoscopic (visual pathway) and macro (visual cortex) levels, this paper constructs a biological dual-channel visual neural network (BDVNN) inspired by the visual characteristics of biological dual-channel for efficient classification of the electromagnetic signals. The input part of BDVNN simulates the dual-channel structure of lateral geniculate nucleus (LGN), building a dual-channel parallel module composed of convolution kernels of different scales to extract the overall coarse features and local fine features from the input signal, respectively. The dual-channel module internally uses atrous convolution to simulate the sparse coding characteristics of LGN, aiming to reduce the model parameters as well as increase the recognition efficiency. The middle part of BDVNN simulates the fusion of primary visual cortex receptive fields to fuse coarse features with fine features, which facilitates the further extraction of advanced features in subsequent layers of the network. In the setting of the number of network layers, the BDVNN model refers to the biological visual pathway hierarchy to meet the lightweight characteristics so as to significantly reduce the training cost. For the RadioML2016.10a dataset, the recognition accuracy of BDVNN is 9.73%, 92.45% and 93.64% under the signal-to-noise ratio of -20dB, 2dB and 18dB, respectively, with 2%~17% improvement over the rest of mainstream algorithms. For the ACARS dataset, the average recognition accuracy of BDVNN for 20 signals is 95.2%, which is also bett

关键词： Network layers

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A microwave photonic prototype for concurrent radar detection and spectrum sensing over an 8 to 40 GHz bandwidth

arXiv

引用

arXiv 2024年

作者： Shi, Taixia Liang, Dingding Wang, Lu Li, Lin Guo, Shaogang Gao, Jiawei Li, Xiaowei Lin, Chulun Shi, Lei Ding, Baogang Liu, Shiyang Yang, Fangyi Jiang, Chi Chen, Yang Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Space Optoelectronic Measurement and Perception Lab Beijing Institute of Control Engineering Beijing100190 China Shanghai Lujie Communication Technology Company Limited Shanghai201613 China

In this work, a microwave photonic prototype for concurrent radar detection and spectrum sensing is proposed, designed, built, and investigated. A direct digital synthesizer and an analog electronic circuit are integrated to generate an intermediate frequency (IF) linearly frequency-modulated (LFM) signal with a tunable center frequency from 2.5 to 9.5 GHz and an instantaneous bandwidth of 1 GHz. The IF LFM signal is converted to the optical domain via an intensity modulator and then filtered by a fiber Bragg grating (FBG) to generate only two 2nd-order optical LFM sidebands. In radar detection, the two optical LFM sidebands beat with each other to generate a frequency-and-bandwidth-quadrupled LFM signal, which is used for ranging, radial velocity measurement, and imaging. By changing the center frequency of the IF LFM signal, the radar function can be operated within 8 to 40 GHz. In spectrum sensing, one 2nd-order optical LFM sideband is selected by another FBG, which then works in conjunction with the stimulated Brillouin scattering gain spectrum to map the frequency of the signal under test to time with an instantaneous measurement bandwidth of 2 GHz. By using a frequency shift module to adjust the pump frequency, the frequency measurement range can be adjusted from 0 to 40 GHz. The prototype is comprehensively studied and tested, which is capable of achieving a range resolution of 3.75 cm, a range error of less than ±2 cm, a radial velocity error within ±1 cm/s, delivering clear imaging of multiple small targets, and maintaining a frequency measurement error of less than ±7 MHz and a frequency resolution of better than 20 MHz. Copyright © 2024, The Authors. All rights reserved.

关键词： Fiber Bragg gratings

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