Yuxin Zhao ¹ Jiahao Wu ^2* Mianjie Zheng ³

• ¹ University of Westminster, London, London, United Kingdom
• ² Guangzhou Xinhua University, Guangzhou, China
• ³ Guangdong Ocean University, Zhanjiang, Guangdong Province, China

Time angle of arrival (AoA) and difference of arrival (TDOA), which determine the position of a moving target object by measuring the arrival angle and the arrival time difference of the signal source, respectively, are two commonly used schemes for solving the dynamic signal source localization (DSSL) systems. For robotic manipulators, accurate and real-time joint information is critical for tasks such as trajectory tracking and visual servoing. In practical application scenarios, they are susceptible to noise interference during the process of signal propagation and acquisition. However, considering the requirement of real-time nature for most DSSL and robotic problems, it is difficult to pre-perform noise reduction. In order to conquer this challenge, a noise-immune zeroing neural dynamics (NIZND) model has been developed and proposed. The proposed noise-immune zeroing neural dynamics represents a brain-inspired algorithm, in which information is transmitted and updated through neurons. This model incorporates a special integral term and an activation function into the traditional ZND model. It aims to effectively address the problem of localizing moving target objects in the presence of noise, striving for a method that synchronously offers high precision and noise suppression performance. Theoretical analysis is given to verify that the proposed NIZND model can still exhibit global convergence and high precision under noisy interference conditions. Furthermore, the robustness, superiority, and effectiveness of the proposed NIZND model are substantiated by some simulation experiments with the two different DSSL-solving schemes and a trajectory tracking scheme for robotic manipulator.