## 1 Introduction  
Structural health monitoring (SHM) has been the subject of different studies in the non-destructive testing (NDT) field aiming to identify the location of an impact point using piezoelectric sensors. Many plate-like geometries made in fiber-reinforced composite (CFRP) or aluminum--commonly used in aerospace structures--are suitable to support the guided propagation of ultrasound-guided waves over long distances; for plate-like structures a few mm thick we refer to extensional and flexural modes of Lamb waves. The possibility of identifying damage on a structure by piezoelectric sensor systems allows for the determination of its integrity, thus reducing downtime and maintenance costs. SHM is important because, by continuous monitoring, it permits real-time detection of damage in a structure, particularly of safety-critical components typical of the application of such compounds in aircrafts [1; 2; 3; 4; 5]. A real-time SHM system with a network of passive sensors was proposed to detect low- and high-impact events. For each event, the conversion of kinetic energy into propagating ultrasonic guided waves occurs and acoustic emission (AE) localization methods can be applied using piezoelectric transducers. Once a localization is performed, the SHM system can be switched to the active mode operation to investigate a restricted area around the estimated impact position. An array of sensors and real-time multichannel electronic processing are the enabling technologies for the application of SHM in aerospace, energy conversion, transportation, and automotive industries. In the literature, different strategies for locating impact positions using Lamb waves were investigated with reference to the extraction of the differential time of arrival (DToA). [PERSON] in [6] used a triangulation technique; [PERSON] and [PERSON] in [7] evaluated the DToA with an algorithm based on continuous wavelet transform (CWT); [PERSON] et al. in [8] used a method based on wavelet transform analysis and modal location (WTML) with four sensors; [PERSON] et al. in [9] used eight sensors bonded in a Z-shaped arrangement. While CWT allows more sophisticated signal processing for the separation of the flexural and extensional dispersive modes, in a multisensory system with several nodes simpler solutions--like the first arrival signal detection with threshold method or short-time Fourier transform (STFT)--must also be considered. The latter requires less computational resources than CWT but does not permit the trade-off between the arrival time and spectral content. For complex three-dimensional structures, analysis of the DToA, relative to propagation along multiple paths, is not straightforward and is thus difficult to determine with analytical models. In these cases, multiple sensors and artificial intelligence (AI) are often used; for example, the system based on neural networks presented by [PERSON] et al. [10] used up to 17 sensors. [PERSON] et al. [11; 12] proposed an innovative method based on nonlinear Lamb waves for locating disbonds in single-lap joints that uses PZT signals processed with a baseline-free algorithm and the localization of artificial defects placed inside the area, delimited by four PZT sensors. Other strategies for locating impact positions, without knowledge of the characteristics of the material under test, are also reported in [13; 14]. In a previous work, [PERSON] et al. [14] presented a study that considered different shapes of the wave front generated during an acoustic event and developed a methodology to localize the acoustic source in an anisotropic plate from those wave front shapes: an elliptical wave front shape-based technique was developed first, followed by the development of a parametric curve-based technique for non-elliptical wave front shapes.