## 1 Introduction
Furthermore, environmental restrictions during aircraft operations limit the number of AE sensors that can be attached to the aircraft, adding complexity to data analysis. Considering these limitations, developing approaches including machine learning are necessary to aid in the real-time analysis of AE data from a minimal number of sensors, in this case, a single AE sensor, and to discern impact locations. Machine learning algorithms, such as support vector machines (SVM) [27], random forests [28], and artificial neural networks (ANN) [29], have been employed to classify AE signals into different classes and identify damage mechanisms and have demonstrated their ability to handle complex datasets and achieve acceptable accuracy in damage identification and damage localization in composite materials. For instance, [PERSON] et al. [27] used SVM to classify AE signals obtained from carbon fiber-reinforced polymers, demonstrating the potential of machine learning in damage assessment. [PERSON] et al. [28] employed random forests to identify the type and severity of defects in composite materials by analyzing AE signals. [PERSON] et al. [29] investigated impact localizations on an aviation control surface. The localization task was considered a classification problem. ANN, random forest, and stacked autoencoder were used to classify AE data obtained from an impacted composite specimen into different regions and achieved acceptable accuracy. The application of machine learning techniques for AE signal analysis in the previous literature highlighted the efficacy of these methods for the impact localization of aircraft control surfaces. Notably, existing research on impact localization of aircraft control surfaces using machine learning focuses on transforming impact localization into a classification problem, where the impact is confined to a relatively broad area. However, there is a gap in achieving accurate impact localization of aircraft control surfaces, specifically focusing on localizing impacts with precision instead of within a broad area. To address this gap, this paper presents a preliminary study that investigates the application of a single AE sensor for localizing impact on aircraft control surfaces. In this preliminary study, the impact energy is assumed to be constant. In contrast to treating localization as a classification issue, this approach employs two regression algorithms, namely, linear regression and random forest,to accurately estimate the location of each impact event. Linear regression and random forest were chosen due to their simplicity and interpretability. The two methods provide a simple and effective way to model the relationship between the AE and impact locations. Moreover, linear regression and random forest models are computationally efficient, which is essential in a real-time SHM system. To assess the efficacy of the proposed method, an impact experiment was carried out on a specimen of an aircraft elevator. The findings of this research demonstrate that the proposed method effectively localizes impacts, thereby substantiating the potential of regression algorithms in enhancing the precision of impact localization for aircraft control surfaces.