Noise-immune zeroing neural dynamics for dynamic signal source localization system and robotic applications in the presence of noise
Time angle of arrival (AoA) and time difference of arrival (TDOA) are two widely used methods for solving dynamic signal source localization (DSSL) problems, where the position of a moving target is determined by measuring the angle and time difference of the signal's arrival, respectively. In...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Neurorobotics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2025.1546731/full |
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| author | Yuxin Zhao Jiahao Wu Mianjie Zheng |
| author_facet | Yuxin Zhao Jiahao Wu Mianjie Zheng |
| author_sort | Yuxin Zhao |
| collection | DOAJ |
| description | Time angle of arrival (AoA) and time difference of arrival (TDOA) are two widely used methods for solving dynamic signal source localization (DSSL) problems, where the position of a moving target is determined by measuring the angle and time difference of the signal's arrival, respectively. In robotic manipulator applications, accurate and real-time joint information is crucial for tasks such as trajectory tracking and visual servoing. However, signal propagation and acquisition are susceptible to noise interference, which poses challenges for real-time systems. To address this issue, a noise-immune zeroing neural dynamics (NIZND) model is proposed. The NIZND model is a brain-inspired algorithm that incorporates an integral term and an activation function into the traditional zeroing neural dynamics (ZND) model, designed to effectively mitigate noise interference during localization tasks. Theoretical analysis confirms that the proposed NIZND model exhibits global convergence and high precision under noisy conditions. Simulation experiments demonstrate the robustness and effectiveness of the NIZND model in comparison to traditional DSSL-solving schemes and in a trajectory tracking scheme for robotic manipulators. The NIZND model offers a promising solution to the challenge of accurate localization in noisy environments, ensuring both high precision and effective noise suppression. The experimental results highlight its superiority in real-time applications where noise interference is prevalent. |
| format | Article |
| id | doaj-art-59e4bcf2e31b4835a7c3f4e8dcacb5f5 |
| institution | Kabale University |
| issn | 1662-5218 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj-art-59e4bcf2e31b4835a7c3f4e8dcacb5f52025-02-05T07:32:43ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182025-02-011910.3389/fnbot.2025.15467311546731Noise-immune zeroing neural dynamics for dynamic signal source localization system and robotic applications in the presence of noiseYuxin Zhao0Jiahao Wu1Mianjie Zheng2School of Humanities, University of Westminster, London, United KingdomSchool of Information and Intelligent Engineering, Guangzhou Xinhua University, Guangzhou, Guangdong, ChinaSchool of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaTime angle of arrival (AoA) and time difference of arrival (TDOA) are two widely used methods for solving dynamic signal source localization (DSSL) problems, where the position of a moving target is determined by measuring the angle and time difference of the signal's arrival, respectively. In robotic manipulator applications, accurate and real-time joint information is crucial for tasks such as trajectory tracking and visual servoing. However, signal propagation and acquisition are susceptible to noise interference, which poses challenges for real-time systems. To address this issue, a noise-immune zeroing neural dynamics (NIZND) model is proposed. The NIZND model is a brain-inspired algorithm that incorporates an integral term and an activation function into the traditional zeroing neural dynamics (ZND) model, designed to effectively mitigate noise interference during localization tasks. Theoretical analysis confirms that the proposed NIZND model exhibits global convergence and high precision under noisy conditions. Simulation experiments demonstrate the robustness and effectiveness of the NIZND model in comparison to traditional DSSL-solving schemes and in a trajectory tracking scheme for robotic manipulators. The NIZND model offers a promising solution to the challenge of accurate localization in noisy environments, ensuring both high precision and effective noise suppression. The experimental results highlight its superiority in real-time applications where noise interference is prevalent.https://www.frontiersin.org/articles/10.3389/fnbot.2025.1546731/fulldynamic signal source localizationrobotic manipulatorangle-of-arrival (AoA) schemetime-difference-of-arrival (TDOA) schemetrajectory tracking schemenoise-immune zeroing neural dynamics (NIZND) |
| spellingShingle | Yuxin Zhao Jiahao Wu Mianjie Zheng Noise-immune zeroing neural dynamics for dynamic signal source localization system and robotic applications in the presence of noise Frontiers in Neurorobotics dynamic signal source localization robotic manipulator angle-of-arrival (AoA) scheme time-difference-of-arrival (TDOA) scheme trajectory tracking scheme noise-immune zeroing neural dynamics (NIZND) |
| title | Noise-immune zeroing neural dynamics for dynamic signal source localization system and robotic applications in the presence of noise |
| title_full | Noise-immune zeroing neural dynamics for dynamic signal source localization system and robotic applications in the presence of noise |
| title_fullStr | Noise-immune zeroing neural dynamics for dynamic signal source localization system and robotic applications in the presence of noise |
| title_full_unstemmed | Noise-immune zeroing neural dynamics for dynamic signal source localization system and robotic applications in the presence of noise |
| title_short | Noise-immune zeroing neural dynamics for dynamic signal source localization system and robotic applications in the presence of noise |
| title_sort | noise immune zeroing neural dynamics for dynamic signal source localization system and robotic applications in the presence of noise |
| topic | dynamic signal source localization robotic manipulator angle-of-arrival (AoA) scheme time-difference-of-arrival (TDOA) scheme trajectory tracking scheme noise-immune zeroing neural dynamics (NIZND) |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2025.1546731/full |
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