# A Novel Localization Technique Using Luminous Flux
## 1 Introduction
Another novel algorithm was proposed for Bluetooth technology in [27] that has a range of errors of roughly 2.4 m. Although the existing techniques used in this literature are novel, the abovementioned technologies are not widely deployable and are not suitable for extensive indoor positioning. Most of the prescribed techniques in the literature are useful for selective object identification, which may require special hardware infrastructure and additional costs. Besides the aforementioned technologies, VLC-based localization techniques were also used by different researchers in [28, 29]. The authors proposed an improved algorithm by applying machine learning techniques to retrieve localization information. More specifically, authors in [30] proposed a scheme for localization estimation by using the Lambertian emission pattern. In this technique, the user measurements from RSS are fitted into an interpreted propagation model. Thus, analytical results are limited to specific settings. A Recent study was published [31] on a technology that comprises infrared beacons, which measure positioning error for both LoS and Non-LoS in an indoor environment. However, this system is not widely deployed because it incurs an extra cost. After a comprehensive study of the existing literature, we have proposed a novel approach to combine PDoP and RSS observations, and provide an explicit relationship between PDoP and the observable degree of root mean square (RMS) error by using the VLC communication channel. Secondly, to fully understand the higher-order terms of impulse response, single and multiple reflection measurements are performed. Additionally, in this study, the precedent estimation of operation, particularly related to the required range, and consequent positional errors result in a propagation environment. VLC channel models for various indoor environments, especially NLoS, are still in the exploratory phase. Authors in [32] stated that out of the total light, around 45% of light is received from NLoS interaction. Moreover, authors in [33] claimed that NLoS occurs in a dense cluttered room or space, resulting in multiple reflections from such objects or surfaces. Various NLoS scheme results significantly differ because of the lack of appropriate and general criteria to evaluate localization error. The DoP has been used by different researchers for indoor positioning in estimating the positions of objects. In practice, it is divided into various elements, such as vertical DoP (VDoP), geometric DoP (GDoP), and positional DoP (PDoP) [34]. All of the abovementioned components are useful for evaluating the accuracy of a GNNS system. The details of DoP- and AoA-based positioning are comprehensively discussed in [35]. The authors evaluated radio signals to ensure the quality of an AoA position and determined those most affected by multipath. Further, the geometric dilution of precision (GDoP) with a machine learning algorithm was used to calculate the estimated value of GDoP in [36], in which the lower approximation error with fewer epochs was observed to estimate the position used by the mobile station. TDoA-based source location in three dimensions (3D) was analyzed with GDoP metrics in [37]. However, it was based solely on the deterministic location of nodes. Here, we will apply VLC as a communication channel in an indoor localization method by using GNNS featuring PDoP, which is a subset of DoP. DoP relates to the measurement error in an indoor environment. This metric can be used to increase the accuracy [38]. The main goal is to investigate and analyze an indoor VLC channel model (for LoS and NLoS). Moreover, we will calculate and explore the impulse response (IR), signal-to-noise ratio (SNR), and the PDoP for RSS-based analysis in an indoor environment. The influence of PDoP in a positioning system under the visible light spectrum is very limited. By using this metric, we can estimate the lower approximation error with fewer epochs in NLoS.