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10th International Conference on control, Automation and Information sciences, ICCAIS 2021

作者： Cao, Ziyi Yuan, Ye Yi, Wei Chen, Linxiu University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781665440295

In this paper, a new power allocation scheme of the distributed radar system is proposed for mini-UAV tracking tasks in urban environments, considering the influence of the building occlusion on the probability of detection. Firstly, the probability of detection of each radar node is derived according to the estimated position of the target and the visibility of each radar node. Then, according to the probability of detection, the adaptive cost function concerning the transmit power and tracking accuracy is designed and used to the formulation of the optimization problem. Simulation results show that the proposed power allocation scheme can improve the target tracking accuracy in urban environments compared to the conventional method. © 2021 IEEE.

关键词： Cost functions

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing 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 for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation system (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Research on Simulation technology of Cooperative Guidance for Aircraft Formation

Research on Simulation Technology of Cooperative Guidance fo...

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Chinese intelligent systems Conference, CISC 2020

作者： Tong, Jiahui Liu, Jin Li, Jing Zhan, Tianming Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China TAIJI Computer Corporation Limited Beijing China

ISBN: (纸本)9789811584572

This paper analyzed the structure and characteristics of cooperative guidance system of aircraft formation, puts forward a multi-level simulation system architecture of centralized/distributed driving to solve the cooperative guidance for aircraft formation, realized the modeling of aircraft formation members and interaction by using multi-agent method, discussed the simulation methods of data link networking transmission and positioning, space-time registration of simulation system, and so on. The simulation is carried out based on a certain combat scenario, and the results show that the simulation system constructed in this paper can simulate the whole process of cooperative guidance of aircraft formation. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer architecture

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Lightweight hybrid visual-inertial odometry with closed-form zero velocity update

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Chinese Journal of Aeronautics 2020年第12期33卷 3344-3359页

作者： QIU Xiaochen ZHANG Hai FU Wenxing School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Science and Technology on Aircraft Control Laboratory Beijing 100191China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing 100074China

Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual *** VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter(MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, onedimensional inverse depth parametrization is utilized to parametrize the augmented feature *** modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene,a novel closed-form Zero velocity UPda Te(ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.

关键词： Inverse depth parametrization Kalman filter Online calibration Visual-inertial odometry Zero velocity update

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Transformer based Online Continuous Multi-Target Tracking with State Regression

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

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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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Meta-ZSDETR: Zero-shot DETR with Meta-learning

Meta-ZSDETR: Zero-shot DETR with Meta-learning

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International Conference on Computer Vision (ICCV)

作者： Lu Zhang Chenbo Zhang Jiajia Zhao Jihong Guan Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China Department of Computer Science & Technology Tongji University China

Zero-shot object detection aims to localize and recognize objects of unseen classes. Most of existing works face two problems: the low recall of RPN in unseen classes and the confusion of unseen classes with background. In this paper, we present the first method that combines DETR and meta-learning to perform zero-shot object detection, named Meta-ZSDETR, where model training is formalized as an individual episode based meta-learning task. Different from Faster R-CNN based methods that firstly generate class-agnostic proposals, and then classify them with visual-semantic alignment module, Meta-ZSDETR directly predict class-specific boxes with class-specific queries and further filter them with the predicted accuracy from classification head. The model is optimized with meta-contrastive learning, which contains a regression head to generate the coordinates of class-specific boxes, a classification head to predict the accuracy of generated boxes, and a contrastive head that utilizes the proposed contrastive-reconstruction loss to further separate different classes in visual space. We conduct extensive experiments on two benchmark datasets MS COCO and PASCAL VOC. Experimental results show that our method outperforms the existing ZSD methods by a large margin.

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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 Interval-Peak-Based Algorithm for Range Image Reconstruction of GM-APD Lidar

An Interval-Peak-Based Algorithm for Range Image Reconstruct...

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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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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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Rudder Health Monitoring and Data Visualization Based on Feature Extraction 10

Rudder Health Monitoring and Data Visualization Based on Fea...

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10th IEEE Data Driven control and Learning systems Conference, DDCLS 2021

作者： Zhou, Yajing Sun, Mingwei Hao, Mingrui Chen, Zengqiang College of Artificial Intelligence Nankai University Tianjin300350 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9781665424233

In order to monitor the health status of the rudder, this paper proposes a health monitoring method based on deep neural network through feature extraction. First, sufficient samples are obtained by data preprocessing. Then the feature dimension is reduced by using autoencoder, and the health monitoring can be carried out by combining with Softmax classifier. After proper tuning and comparison, proper network structure parameters can be obtained. Two kinds of images can be plotted by using the feature extraction of encoder to provide explicit contrast between the normal and abnormal rudders. The effectiveness of the proposed method can be demonstrated by numerical simulations. © 2021 IEEE.

关键词： Visualization

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