基于随机霍夫变换与模型拟合的传感器阵列数据鲁棒波源检测
Robust Wave Origin Detection from Sensor Array Data via Randomized Hough Transform and Model Fitting
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中文总结 AI 辅助
针对异质介质和传感器网络下波源检测难题，提出结合巴特沃斯滤波、三维随机霍夫变换和最小二乘模型拟合的鲁棒框架，在仿真和真实数据中展现优越鲁棒性与效率。
中文摘要 AI 辅助
表征波状传播信号源的方法的鲁棒性常常受到传输介质和传感器网络异质性的挑战。在这种噪声条件下检测单个波仍然特别困难。本文提出了一种鲁棒框架，用于从传感器阵列收集的时空数据中估计波源。首先，设计了一个巴特沃斯滤波器，从每个传感器通道中提取相关信号。其次，引入了三维随机霍夫变换，以识别源自同一波前的候选信号。最后，使用最小二乘法将波传播模型拟合到已识别的信号上。通过将随机霍夫变换的迭代投票机制与最小二乘优化相结合，我们提出的方法在仿真研究和真实数据分析中均展现出优越的鲁棒性和计算效率。
英文摘要
The robustness of methods for characterizing the origin of wave-like propagating signals is often challenged by the heterogeneity of transmitting media and sensor network. Detecting an individual wave under such noisy conditions remains particularly difficult. In this paper, we propose a robust framework to estimate wave origins from spatiotemporal data collected by a sensor array. First, a Butterworth filter is designed to extract relevant signals from each sensor channel. Next, a 3D randomized Hough transform is introduced to identify candidate signals originating from the same wave front. Finally, a wave propagation model is fitted to the identified signals using the least-squares method. By combining the iterative voting mechanism of the randomized Hough transform with least-squares optimization, our proposed method demonstrates superior robustness and computational efficiency in both simulation studies and real-world data analyses.
发表机构
- Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal University（华南师范大学阿伯丁数据科学与人工智能研究所）
- Department of Statistics and Data Science, The Chinese University of Hong Kong（香港中文大学统计与数据科学系）
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