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第43届中国控制会议

作者： Tao Wu Xiang Xu Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

controlling a PDE-ODE cascade system with input delay is often challenging even for the case where both PDE and ODE are not complex. This challenge mainly arises due to the existence of input delay, which often leads to poor performance and instability. In this work, we design a state feedback controller for such a system, that is, a PDE-ODE system with input *** controller demonstrates the ability to exponentially stabilize the PDE-ODE cascade system, provided that a carefully chosen parameter is sufficiently small. The proof of stability is established through the construction of a Lyapunov function. Finally, we provide a simulation example that illustrates the effectiveness of our proposed approach.

关键词： PDE-ODE cascade system Time delays Boundary control

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Information-Aware Joint Calibration of Microphone Array and Sound Source Localization

Information-Aware Joint Calibration of Microphone Array and ...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Xiaoyang Li Haowen Deng Jiang Wang Linya Fu He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350366402

ISBN: (纸本)9798350366419

Accurate calibration of microphone arrays is essential for various applications. However, existing graph SLAM-based methods, which introduce additional poselandmark constraints to enhance calibration accuracy, face challenges related to data redundancy and increased computational costs. In this paper, we propose a novel approach that combines the graph SLAM framework with an information-theoretic data selection strategy for efficient joint microphone array calibration and sound source localization. Our method evaluates and selects the most informative data segments by leveraging mutual information, effectively filtering out redundant measurements. This approach results in a streamlined and efficient calibration dataset that facilitates high estimation accuracy while significantly reducing the computational burden. Extensive simulations and real-world experiments validate that our method outperforms existing full batch data processing methods, demonstrating its potential for realtime applications. All the codes and datasets are publicly available at https://***/AISLAB-sustech/IA-Microphone-Calibration.

关键词： Location awareness Accuracy Simultaneous localization and mapping Computational modeling Redundancy Microphone arrays Calibration Computational efficiency Arrays Mutual information

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Asynchronous Microphone Array Calibration using Hybrid TDOA Information

Asynchronous Microphone Array Calibration using Hybrid TDOA ...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Chengjie Zhang Jiang Wang He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Asynchronous microphone array calibration is a prerequisite for many audition robot applications. A popular solution to the above calibration problem is the batch form of Simultaneous Localisation and Mapping (SLAM), using the time difference of arrival measurements between two microphones (TDOA-M), and the robot (which serves as a moving sound source during calibration) odometry information. In this paper, we introduce a new form of measurement for microphone array calibration, i.e. the time difference of arrival between adjacent sound events (TDOA-S) with respect to the microphone channels. We propose to use TDOA-S and TDOA-M, called hybrid TDOA, together with odometry measurements for bath SLAM-based calibration of asynchronous microphone arrays. Extensive simulation and real-world experiments show that our method is more independent of microphone number, less sensitive to initial values (when using off-the-shelf algorithms such as Gauss-Newton iterations), and has better calibration accuracy and robustness under various TDOA noises. Simulation results also demonstrate that our method has a lower Cramér-Rao lower bound (CRLB) for microphone parameters. To benefit the community, we open-source our code and data at https://***/AISLAB-sustech/Hybrid-TDOA-Calib.

关键词： Simultaneous localization and mapping Accuracy Time difference of arrival Noise Microphone arrays Time measurement Robustness Calibration Odometry Arrays

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Improved Extrinsic Calibration of Acoustic Cameras via Batch Optimization

Improved Extrinsic Calibration of Acoustic Cameras via Batch...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhi Li Jiang Wang Xiaoyang Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems and the Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Acoustic cameras have found many applications in practice. Accurate and reliable extrinsic calibration of the microphone array and visual sensors within acoustic cameras is crucial for fusing visual and auditory measurements. Existing calibration methods either require prior knowledge of the microphone array geometry or rely on grid search which suffers from slow iteration speed or poor convergence. To overcome these limitations, in this paper, we propose an automatic calibration technique using a calibration board with both visual and acoustic markers to identify each microphone position in the camera frame. We formulate the extrinsic calibration problem (between microphones and the visual sensor) as a nonlinear least squares problem and employ a batch optimization strategy to solve the associated problem. Extensive numerical simulations and real-world experiments show that the proposed method improves both the accuracy and robustness of extrinsic parameter calibration for acoustic cameras, in comparison to existing methods. To benefit the community, we open-source all the codes and data at https://***/AISLAB-sustech/AcousticCamera.

关键词： Visualization Accuracy Cameras Numerical simulation Acoustics Robustness Microphone arrays Calibration Optimization Sensor arrays

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Calibration of Multiple Asynchronous Microphone Arrays using Hybrid TDOA

Calibration of Multiple Asynchronous Microphone Arrays using...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chengjie Zhang Wenda Pan Xinyang Han He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems and the Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurate calibration of acoustic sensing systems made of multiple asynchronous microphone arrays is essential for satisfactory performance in sound sour ce localization and tracking. State-of-the-art calibration methods for this type of system rely on the time difference of arrival and direction of arrival measurements among the microphone arrays (denoted as TDOA-M and DOA, respectively). In this paper, to enhance calibration accuracy, we propose to incorporate the time difference of arrival measurements between adjacent sound events (TDOA-S) with respect to the microphone arrays. More specifically, we propose a two-stage calibration approach, including an initial value estimation (IVE) procedure and the final joint optimization step. The IVE stage first initializes all parameters except for microphone array orientations, using hybrid TDOA (i.e., TDOA-M and TDOA-S), odometer data from a moving robot carrying a speaker, and DOA. Subsequently, microphone orientations are estimated through the iterative closest point method. The final joint optimization step estimates multiple microphone array locations, orientations, time offsets, clock drift rates, and sound source locations simultaneously. Both simulation and experiment results show that for scenarios with low or moderate TDOA noise levels, our approach outperforms existing methods in terms of accuracy. All code and data are available at https://***/AISLAB-sustech/Hybrid-TDOA-Multi-Calib.

关键词： Direction-of-arrival estimation Accuracy Time difference of arrival Position measurement Odometers Microphone arrays Calibration Arrays Optimization Noise level

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I-ASM: Iterative Acoustic Scene Mapping for Enhanced Robot Auditory Perception in Complex Indoor Environments

I-ASM: Iterative Acoustic Scene Mapping for Enhanced Robot A...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Linya Fu Yuanzheng He Jiang Wang Xu Qiao He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper addresses the challenge of acoustic scene mapping (ASM) in complex indoor environments with multiple sound sources. Unlike existing methods that rely on prior data association or SLAM frameworks, we propose a novel particle filter-based iterative framework, termed I-ASM, for ASM using a mobile robot equipped with a microphone array and LiDAR. I-ASM harnesses an innovative "implicit association" to align sound sources with Direction of Arrival (DoA) observations without requiring explicit pairing, thereby streamlining the mapping process. Given inputs including an occupancy map, DoA estimates from various robot positions, and corresponding robot pose data, I-ASM performs multi-source mapping through an iterative cycle of "Filtering-Clustering-Implicit Associating". The proposed framework has been tested in real-world scenarios with up to 10 concurrent sound sources, demonstrating its robustness against missing and false DoA estimates while achieving high-quality ASM results. To benefit the community, we open-source all the codes and data at https://***/AISLAB-sustech/Acoustic-Scene-Mapping

关键词： Direction-of-arrival estimation Simultaneous localization and mapping Refining Acoustics Robustness Microphone arrays Indoor environment Iterative methods Mobile robots Resilience

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Calibration of Multiple Asynchronous Microphone Arrays using Hybrid TDOA

arXiv

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arXiv 2025年

作者： Zhang, Chengjie Pan, Wenda Han, Xinyang Kong, He Shenzhen Key Laboratory of Control Theory and Intelligent Systems The Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology The Southern University of Science and Technology Shenzhen518055 China

Accurate calibration of acoustic sensing systems made of multiple asynchronous microphone arrays is essential for satisfactory performance in sound source localization and tracking. State-of-the-art calibration methods for this type of system rely on the time difference of arrival and direction of arrival measurements among the microphone arrays (denoted as TDOA-M and DOA, respectively). In this paper, to enhance calibration accuracy, we propose to incorporate the time difference of arrival measurements between adjacent sound events (TDOA-S) with respect to the microphone arrays. More specifically, we propose a two-stage calibration approach, including an initial value estimation (IVE) procedure and the final joint optimization step. The IVE stage first initializes all parameters except for microphone array orientations, using hybrid TDOA (i.e., TDOA-M and TDOA-S), odometer data from a moving robot carrying a speaker, and DOA. Subsequently, microphone orientations are estimated through the iterative closest point method. The final joint optimization step estimates multiple microphone array locations, orientations, time offsets, clock drift rates, and sound source locations simultaneously. Both simulation and experiment results show that for scenarios with low or moderate TDOA noise levels, our approach outperforms existing methods in terms of accuracy. All code and data are available at https://***/AISLABsustech/Hybrid-TDOA-Multi-Calib. © 2025, CC BY.

关键词： Microphone array

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Topology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 16298-16313页

作者： Li, Mengran Chen, Junzhou Yu, Chenyun Jiang, Guanying Zhang, Ronghui Shen, Yanming Song, Houbing Herbert Shenzhen Campus of Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen518107 China Baidu Inc Beijing100085 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education School of Electronic Information and Electrical Engineering Dalian116024 China Department of Information Systems Baltimore County BaltimoreMD21250 United States

With the advancement of information technology, the Social Internet of Things (SIoT) has fostered the integration of physical devices and social networks, deepening the study of complex interaction patterns. Text Attribute Graphs (TAGs) capture both topological structures and semantic attributes, enhancing the analysis of complex interactions within the SIoT. However, existing graph learning methods are typically designed for complete attributed graphs, and the common issue of missing attributes in Attribute Missing Graphs (AMGs) increases the difficulty of analysis tasks. To address this, we propose the Topology-Driven Attribute Recovery (TDAR) framework, which leverages topological data for AMG learning. TDAR introduces an improved pre-filling method for initial attribute recovery using native graph topology. Additionally, it dynamically adjusts propagation weights and incorporates homogeneity strategies within the embedding space to suit AMGs' unique topological structures, effectively reducing noise during information propagation. Extensive experiments on public datasets demonstrate that TDAR significantly outperforms state-of-the-art methods in attribute reconstruction and downstream tasks, offering a robust solution to the challenges posed by AMGs. The code is available at https://***/limengran98/TDAR. © 2014 IEEE.

关键词： Undirected graphs

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Asynchronous Microphone Array Calibration using Hybrid TDOA Information

arXiv

引用

arXiv 2024年

作者： Zhang, Chengjie Wang, Jiang Kong, He Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen518055 China

关键词： Calibration

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Multi-Agent Reinforcement Learning with Region Division and Leader-Follower Communication

Multi-Agent Reinforcement Learning with Region Division and ...

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Chinese Automation Congress (CAC)

作者： Chao Wang Xiang Xu Shenzhen Key Laboratory of Control Theory Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The integration of multi-agent reinforcement learning (MARL) into complex systems has paved new ways for collaborative problem-solving. However, traditional approaches to MARL frequently encounter the challenge of achieving ef-ficient communication among agents, essential for coordinated action. This paper introduces a region division and leader-follower(RDLF) communication algrithm with the MARL frame-work. RDLF divides the environment into several regions, each managed by a leader agent that coordinates the actions of fol-lower agents and handling inter-region communication. This hier-archical structure reduces unnecessary communication, enhancing learning efficiency. Experimental results in multi-particle en-vironments demonstrate RDLF's superiority over existing MARL algorithms, especially with increasing agent numbers. RDLF effectively addresses scalability and communication challenges in large-scale multi-agent systems, providing a robust foundation for its application in complex and dynamic environment.

关键词： Automation Scalability Heuristic algorithms Collaboration Reinforcement learning Problem-solving Complex systems Multi-agent systems

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