Fortunately, there is a myriad of AoA methods [13]; the key methods are as follows: conventional beamforming (utilizes an array of antennas to form a beam pattern, focusing on a specific direction; the AoA is estimated by steering the beam in different directions and identifying the direction with the maximum received signal power); multiple signal classification (MUSIC) uses the eigen structure of the covariance matrix of received signals to identify signal directions [14]; estimation of signal parameters via rotational invariance techniques (ESPRIT) [15]; Capon's Method [16] is based on an adaptive beamforming technique that enhances resolution by forming a spatial filter, focusing on minimizing interference and noise while maintaining the signal from a particular direction); the matrix pencil method [17] uses a matrix decomposition approach to estimate the AoA by considering the signal as a sum of complex exponentials; Time Difference of Arrival (TDOA) measures the difference in the time that it takes for a signal to reach different sensors; AoA measurements using machine learning [18] techniques like neural networks and support vector machines are employed; finally, phase interferometry [19] measures the phase difference of received signals at different antennas to estimate the AoA. Simple AoA is most likely to act just on a plane, giving information about the direction of the source/target, without the elevation/azimuth of the source. Three-dimensional; antenna arrays, such as an L-shaped antenna array [20], can offer both direction and elevation information. As will be shown in later sections, a hybrid-ISL (H-ISL) system was proposed that can perform (i) precision range measurements based on the combination of a course and fine range estimation and (ii) LoS estimation based on the simplest L-shaped antenna array. Furthermore, the sensitivity of LoS estimation to measurement errors in the H-ISL system is assessed. Our previous study in [6] is providing valuable contributions to the state of the art as follows:  
* The 3D localization model applicable for the H-ISL system in the Cartesian coordinate system is derived;
* The H-ISL setup based on an L-shaped antenna array enables two kinds of range--coarse and fine--measurement and LoS estimation;
* A sensitivity analysis is conducted to evaluate the impact of ranging and phase measurement errors on LoS estimation. This work extends our previous study presented in [6], offering significant contributions to the state of the art in the following ways:  
* Initial work on peer-to-peer communication within the IOS domain, outlining the development scope of the L-shaped phased array;
* A network protocol stack is proposed to address key limitations of conventional ISL systems, including the following: (i) support for time division multiple access (TDMA) among more than two devices; (ii) integration of a metrology channel for range measurements; and (iii) network-wide time distribution;
* Coarse range measurements are performed via round-trip time (RTT) estimation in the baseband processor, following a calibration phase;
* Fine range measurements are achieved through phase-based techniques, utilizing frequency diversity for phase disambiguation;
* LoS estimation is enabled by combined ranging and phase difference measurements. The main finding of our theoretical analysis is related to the sensitivity analysis of the LoS measurement to the inherent measurement errors. The sensitivity analysis allows design engineers to assess quantitatively the precision and accuracy of the measurement system in the presence of measurement errors. This paper is structured as follows: First, the mathematical model for the 3D positioning system using an L-shaped antenna array is presented. Next, the H-ISL device and its corresponding network protocol are discussed, with a focus on the accuracy of ranging and phase measurements. A key research question explored is the extent to which measurement errors impact LoS precision and accuracy.