## 1 Introduction  
Plate-like structures are omnipresent in different fields such as civil engineering and aerospace with different applications including bridge girders, aircraft wings, fuselages, etc. [1; 2]. Depending on the involved materials, damage can be of different kinds. Corrosion, fatigue cracking, and impacts are among the most significant threats that are encountered most frequently [3]. Among the metallic structures, those made of aluminum, including aluminum alloys, are very attractive thanks to their mechanical high strength and physical corrosion resistance properties [4; 5]. At the time where the most common defects (i.e., cracks) created within aluminum structures and components can be detected reliably using different non-destructive techniques (ultrasound, radiography, etc. ), those created during their manufacturing process (non-metallic inclusions, small pores, etc.) are not easily detected on time and can thus be at the origin of severe crack creation [6; 7]. In order to account for the deterioration of the various structures, different structural health monitoring (SHM) techniques were developed. SHM is motivated by the need to perform permanent monitoring to diagnose the state of structures especially when the application of classical testing methods is difficult or impossible [8; 9]. Among SHM techniques, those based on the use of piezoelectric sensors in a passive mode have undergone significant development. Indeed, the deployment of a network of sensors allows to receive acoustic emission (AE) hits (i.e., elastic waves) emitted during the creation and propagation of damage (i.e., cracks) in order to localize AE sources in plate-like structures [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. Depending on the required level of analysis, AE based SHM can be used to detect defects, and/or localize their position and orientation, and/or assess their size. In time, these three levels can be used to estimate the remaining lifetime of the monitored structures in real time [8]. Time and/or frequency domain methods have already been used to analyze AE hits in order to define reliable parameters during the monitoring of structures [22; 23]. Model-based approaches have also been proposed for damage/impact location and identification in different structures [24]. In the latter, the location and type of impact are iteratively changed until convergence is reached between the experimental results and those predicted by the proposed model. [PERSON] and [PERSON] [25] used [PERSON]'s iterative method to calculate the coordinates of the impact location and the wave velocity. Other contributions used the nonlinear least square optimization technique, based on the Gauss-Newton method, to determine the location, time lag, and velocity of AE signals as performed in refs [26; 27]. In order to minimize errors related to wave velocity, [PERSON] et al. proposed an iterative solution, which does not depend on the P-wave velocity [28]. In the latter, the location accuracy has been improved by taking advantage of the multiple sensors. More recently, [PERSON] et al. proposed an algebraic solution for AE source localization without premeasuring wave velocity. This is performed by minimizing the sum of squared residuals of the linear time difference of arrival (TDOA) equations [29]. On the other hand, [PERSON] et al. used multiple iterations to optimize the clustering in the development of the Generalized Regression Neural Network Based on Time Difference Mapping [30]. Iterative solvers have also been used and compared with direct solvers to perform an automatic three-dimensional source localization of acoustic emission sources within large concrete structures [31]. [PERSON] et al. proposed a nonlinear method to locate acoustic emission sources, in which the sensors with high residuals are first quarantined. Then, the spacial location and event time are iteratively optimized along with the updating of velocity with the help of a feedback loop [32].