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Academic International Symposium on Optoelectronics and Microelectronics technology (AISOMT)

作者： Shihang Sun Jianfeng Sun Peng Jiang Hongchao Ni Xin Zhou Le Ma Ruobin Lin National Key Laboratory of Laser Spatial Information Harbin Institute of Technology Harbin Heilongjiang China Zhengzhou Research Institute Harbin Institute of Technology Zhengzhou Henan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9798331542283

ISBN: (纸本)9798331542290

GM-APD lidar has single-photon sensitivity, so that it can obtain 3D information of distant targets. On the other hand, the laser echo of targets is easily drowned by noise. In order to solve this problem, an interval-peak-based algorithm for range image reconstruction is proposed in this paper. Firstly, in the photon counts histogram, the x-axis is divided into small intervals and the peak value of each interval is taken. This step weakens the bad characteristic caused by the trigger probability. Secondly, according to the data feature, the trigger probability ratio coefficient and the neighborhood correlation coefficient are obtainned. Finally, the range image can be extracted by the peak method after the peak value of each interval is multiplied with this two coefficients. It is verified by the experimental data that when the SNR of target echo data is 0.022, the recovery ratio of the proposed algorithm can reach 80%, which is 25% higher than the matching filter algorithm. The proposed algorithm has a good application prospect in the range image reconstruction of GM-APD lidar.

关键词： Laser radar Three-dimensional displays Sensitivity Filtering Logic gates Approximation algorithms Spatiotemporal phenomena Image reconstruction Signal to noise ratio Photonics

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Gm-APD LiDAR Reconstruction Algorithm with Improved Matched Filtering Based on Negative Binomial Distribution

Gm-APD LiDAR Reconstruction Algorithm with Improved Matched ...

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Academic International Symposium on Optoelectronics and Microelectronics technology (AISOMT)

作者： Hengchang Ou Jianfeng Sun Le Ma Peng Jiang National Key Laboratory of Laser Spatial Information Harbin Institute of Technology Harbin Heilongjiang China Zhengzhou Research Institute Harbin Institute of Technology Zhengzhou Henan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9798331542283

ISBN: (纸本)9798331542290

This article addresses the challenges faced by Gm-APD detectors in lidar systems when detecting weak signals. We use a triggering model based on the negative binomial distribution, which replaces the traditional Poisson distribution model, allowing for a more accurate description of the echo characteristics from rough surface targets. Building on this foundation, we have improved the conventional matched filtering algorithm by integrating the laser pulse waveform with the actual triggering response of the detector, thereby enhancing the detection rate. Experimental results demonstrate that the improved algorithm significantly increases the detection probability under low signal-to-noise ratios and with a limited number of frames, confirming the effectiveness of the new triggering model and the enhanced algorithm.

关键词： Maximum likelihood estimation Laser radar Accuracy Filtering Detectors Filtering algorithms Surface roughness Rough surfaces Photonics Signal to noise ratio

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An Improved RT-DETR-Based Object Detection Algorithm for Gm-APD LiDAR

An Improved RT-DETR-Based Object Detection Algorithm for Gm-...

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Academic International Symposium on Optoelectronics and Microelectronics technology (AISOMT)

作者： Qianxin Wang Jianfeng Sun Peng Jiang Xin Zhou Yuanxue Ding Guanlin Chen National Key Laboratory of Laser Spatial Information Harbin Institute of Technology Harbin Heilongjiang China Zhengzhou Research Institute Harbin Institute of Technology Zhengzhou Henan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9798331542283

ISBN: (纸本)9798331542290

Gm-APD LiDAR has a high sensitivity response characteristic, enabling imaging for detecting targets at extended distances, and finds broad utility. However, the highly sensitive response characteristic also makes Gm-APD LiDAR vulnerable to noise, resulting in poor imaging quality and challenging the application of target detection algorithms to this type of LiDAR. Therefore, this paper proposes a long-range vehicle detection model DRT-DETR for Gm-APD LiDAR. DRT-DETR is based on RT-DETR and employs a dual-channel feature extraction backbone, which combines with the lightweight FasterNet Block to capture semantic features from distance and intensity images effectively. It also incorporates mixed attention modules to selectively combine and enhance key features from both types of images. Attention-based Intrascale Feature Interaction employs cascaded group attention as a replacement for multi-head attention in order to minimize computational redundancy. Finally, Dysample is used instead of nearest neighbor upsampling in the cross-scale feature fusion to align features better and minimize distortion. The improved model boosts detection accuracy by 3.8% over RT-DETR and outperforms other common object detection models on the dataset, demonstrating its effectiveness.

关键词： Laser radar Sensitivity Computational modeling Vehicle detection Semantics Redundancy Imaging Object detection Feature extraction Distortion

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Scheduling optimization of silicon single crystal production process based on adaptive improved particle swarm optimization

Scheduling optimization of silicon single crystal production...

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2021 China Automation Congress, CAC 2021

作者： Kang, Lu Liu, Ding Wu, Yali Ping, Guozheng School of Automation and Information Engineering Xi'an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China School of Automation and Information Engineering Xi'an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China School of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China

ISBN: (纸本)9781665426473

Aiming at the potential safety hazards of silicon single crystal production enterprises in the process of multi-furnace silicon single crystal production, and considering the maximum power load requirements in actual enterprises, a silicon single crystal production process scheduling model with the goal of minimizing the maximum completion time was established. Based on this model, an adaptive improved particle swarm optimization algorithm (Adaptive GPSO) is proposed to solve it. The algorithm retains the individual optimal value and global optimal value of the particle swarm algorithm, and introduces the crossover operation and mutation operation in the genetic algorithm to improve the new individual generation mechanism. At the same time, it performs dynamic iterative calculations on the mutation probability and crossover probability to avoid the algorithm fall into the local optimum, and improve the diversity and convergence of the algorithm. By solving different scales scheduling problems of silicon single crystal production process, the experiments on real-world data shows that the feasibility and effectiveness of the proposed algorithm. © 2021 IEEE

关键词： Genetic algorithms

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Condition Monitoring for Railway Point Machines Based on Sound Analysis and Support Vector Machine

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Chinese Journal of Electronics 2020年第4期29卷 786-792页

作者： SUN Yongkui CAO Yuan XIE Guo WEN Tao School of Electronic and Information Engineering Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Railway point machines(RPMS) are one of the key equipments in the railway system to switch different routes for the *** monitoring for RPMs is a vital measure to keep train operation safe and *** convenience and low cost into consideration, a novel intelligent condition monitoring method for RPMs based on sound analysis is ***-domain and frequency-domain features are obtained,and normalized using z-score standardization method to eliminate the influences of different *** particle swarm optimization(BPSO) is utilized to select the most significant discrimination feature *** effects of the selected optimal features are verified using Support vector machine(SVM), 1-Nearest neighbor(1 NN), Random forest(RF), and Naive Bayes(NB).Experiment results indicate SVM performs best on identification accuracy and computing cost compared with the other three *** identification accuracies on normal switching and reverse switching processes reach100% and 99.67%, respectively, indicating the feasibility of the proposed method.

关键词： Condition monitoring Sound analysis Railway point machines(RPMs) Binary particle swarm optimization(BPSO) Support vector machine(SVM)

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Reverse Time Migration of Ultra-Wideband Ground-Penetrating Radar Based on Multiple Time-Gating Algorithm

Reverse Time Migration of Ultra-Wideband Ground-Penetrating ...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Junjie Wu Haixiao Li Yuanguo Zhou Le Cao Yan Wang College of Communication and Information Engineering Xi’an University of Science and Technology Xi’an China College of Electrical and Control Engineering Xi’an University of Science and Technology Xi’an China Graduate School of Chinese Aeronautical Establishment Yangzhou China Aviation Key Laboratory of Science and Technology on Electromagnetic Environment Effects Shenyang China National Key Laboratory of Electromagnetic Information Control and Effects Shenyang China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

To address the issue of clutter interference in ground-penetrating radar (GPR) imaging using a VNA (Vector Network Analyzer), this paper improves the traditional reverse time migration (RTM) technique for ultra-wideband GPR imaging. The improved method retains the effective signals in the detection wavefield as the valid imaging area and suppresses signals in non-valid areas. Due to variations in the distance between the transmitting and receiving antennas and the number of anomalies in the experiment, the traditional time window cross-correlation is not ideal for this scenario. The improved method automatically applies reasonable windowing to each data channel, avoiding the simultaneous enhancement of clutter interference and effective signals through direct stacking, and preventing the filtering out of effective signals. Experimental results demonstrate that this method, when applied to real measured data and compared with the traditional cross-correlation RTM method, shows advantages in more accurately locating anomalous areas and suppressing clutter interference. This validates that the Multiple Time-Gating Reverse Time Migration (MTG-RTM) method offers better localization and higher imaging accuracy in practical engineering applications.

关键词： Location awareness Accuracy Ground penetrating radar Transmitting antennas Stacking Radar imaging Vectors Reflection Clutter Ultra wideband technology

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An IMU Error Correction Method Based on Multithreshold ZV Detection and SRCKF

An IMU Error Correction Method Based on Multithreshold ZV De...

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Luo, Jialiang Chen, Shichao Lv, Yisheng Sun, Wenqiao Yang, Fan Xu, Ruijie Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Rail Transit Shan Dong JiaoTong University The State Key Laboratory for Management and Control of Complex System Jinan China The Hong Kong Polytechnic University Department of Aeronautical and Aviation Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350312492

With the rapid development and widespread application of Inertial Measurement Units (IMUs), IMU-based human localization has become critically important in environments lacking Global Navigation Satellite systems (GNSS) signals. Methods such as UWB/WIFI and other anchor-based localization have been employed to enhance the positioning accuracy. However, these methods are susceptible to the environmental interference and increased costs. In contrast, SINS-based localization methods eliminate reliance on the external data. However, this method can suffer from drift errors due to cumulative integration errors. Inertial navigation error correction techniques like Zero Detection Update technology (ZUPT) provide potential solutions to address drift errors. This paper proposes an error correction method based on the multithreshold zero-velocity detection and Square Root Cubature Kalman Filter (SRCKF) updates. Compared to the single fixed threshold detection, the multi-threshold zero-velocity detection reduces the likelihood of missed zero velocity detection and false alarms, providing accurate time intervals for error correction. Experimental results demonstrate that the SRCKF-based algorithm reduces position deviation to approximately 0.3146 meters, corresponding to an error ratio of1.14%. The proposed method enables cost-effective and accurate pedestrian localization, driving advancements in the field. © 2023 IEEE.

关键词： Cost effectiveness

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Coastal Wind Speed Retrieval From HY-2C Scatterometer Based On Light Gradient Boosting Model

Coastal Wind Speed Retrieval From HY-2C Scatterometer Based ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Biao Zhang Jingyi Liu Lanjie Zhang Xuehua Li Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China Key Laboratory of Microwave Remote Sensing National Space Science Center Chinese Academy of Sciences Beijing China

In this paper, the sea surface wind speed (SSWS) retrieval accuracy for HaiYang-2C (HY-2C) scatterometer in the coastal regions is evaluated. To improve coastal SSWS accuracy, we use the European Centre for Medium-range Weather Forecast Reanalysis 5th Generation (ECMWF ERA5) data as the reference output to establish the coastal multi-interval SSWS retrieval model based on the light gradient boosting model (LGBM). Compared with the ERA5 wind speed, the root mean square error (RMSE) of the LGBM-based model in the low, medium, and high wind speed ranges is 36.36%, 33.33%, and 34.52% lower than that of the HY-2C wind product, respectively. This paper also compares the SSWS retrieval results with the national Data Buoy Center buoy measurements. Compared to the HY-2C L2B wind product, the RMSE is reduced by 21.05%. This paper provides a dependable reference to coastal SSWS retrieval of HY-2C scatterometer observations.

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Simulation and analysis of far-field single-photon response model in Geiger avalanche mode 7

Simulation and analysis of far-field single-photon response ...

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7th Global Intelligent Industry Conference, GIIC 2024

作者： Ou, Hengchang Ma, Le Jiang, Peng Wang, Pengfei Lu, Jie Zhou, Xin Sun, Jianfeng National Key Laboratory of Laser Spatial Information Institute of Opto-Electronic Harbin Institute of Technology Heilongjiang Harbin150001 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Institute of Remote Sensing Beijing100854 China 44th Research Institute China Electronics Technology Group Corporation Chongqing400060 China Research Center for Space Optical Engineering Harbin Institute of Technology Heilongjiang Harbin150001 China Zhengzhou Research Institute of Harbin Institute of Technology Zhengzhou Henan450046 China

ISBN: (纸本)9781510682986

Poisson distribution model is the basis of data analysis for GM-APD lidar, but it is only applicable to the mirror reflected target under ideal conditions, and cannot accurately describe the photon triggering process of actual GM-APD lidar detection. For the actual target with rough surface, the negative binomial distribution with M as the parameter can describe the photon distribution model more accurately. In order to solve this problem, this paper compares and analyzes Poisson distribution triggering model and the negative binomial distribution triggering model that conforms to the triggering situation of the echo signal of the target with rough surface, considering the differences in the triggering probability under different noise and signal levels. The results show that the trigger probability curve corresponding to the trigger model based on negative binomial distribution is lower in peak value, wider in bottom value and fatter overall than that of the trigger model based on Poisson distribution, and the difference between the two is more prominent under the conditions of low noise level and high signal level. This study has guiding significance for the signal extraction research based on different surface echoes. © 2024 SPIE.

关键词： Photons

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Coastal SEA Surface Salinity Retrieval Analysis from SMAP Mission using Machine Learning

Coastal SEA Surface Salinity Retrieval Analysis from SMAP Mi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： YanFang Lv YiFan Zhang JingYi Liu LanJie Zhang Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China Key Laboratory of Microwave Remote Sensing National Space Science Center Chinese Academy of Sciences Beijing China

Sea surface salinity (SSS) plays an essential role in the study of global climate change. Especially, high accuracy coastal SSS is of great significance for marine aquaculture and other aspects. SMAP is a high resolution satellite equipped with radars and radiometers for measuring soil moisture, which can also achieve observations of SSS. Based on SMAP L2B V5.0 data, to improve the coastal SSS retrieval accuracy, this paper proposes a method for retrieving SSS in coastal regions using deep neural networks (DNN) and analyzes the impact of different variables on retrieving SSS. By comparing the retrieval results of DNN with HYCOM SSS and in situ SSS, the root mean square errors (RMSE) of DNN SSS were 0.36 psu and 0.45 psu, respectively. Compared to SMAP SSS products from physical retrieval algorithm, the RMSE of DNN SSS decreased by 1.38 psu and 1.23 psu, respectively. The results indicate that using DNN to retrieve SSS in coastal regions can achieve high accuracy.

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