# A Novel Localization Technique Using Luminous Flux
## 1 Introduction  
Indoor positioning systems are of great interest to researchers due to the need to deliver services (i.e., store mapping, routes, floor-associated pinning) to users according to their locations. Global navigation satellite systems (GNNS) are widely implemented around the globe. However, GNNS signals are unable to penetrate through walls, specifically in indoor scenarios and urban canyons [1, 2]. These indoor spaces and many urban canyons remain uncovered in existing location-based systems. Hence, numerous technologies have been proposed to address this issue in existing literature, including wireless fidelity (WiFi), ZigBee, Bluetooth, ultra-wide band (UWB), and radio frequency identification (RFID). The problem of localization in indoor scenarios can also be solved by using visible light communication (VLC), which shows much better positioning accuracy. Generally, VLC-based indoor localization is measured by using a visible light signal [3] that includes factors such as light-emitting diode (LED) positions, receiver orientation, light incident angles, and light arrival angles. All of theabovementioned factors are extracted from the light signal. Moreover, the VLC-based localization technique has two exclusive advantages. (i) Optical frequencies in the visible light range from 430 THz to 770 THz can be easily regulated to avoid congestion, and this is a license-free range. Furthermore, the available bandwidth for communication in this range (i.e., 340 THz) is a thousand times larger than the combined 300 GHz bandwidth of radio waves and microwaves (i.e., Ultra High Frequency UHF, Extremely High Frequency EHF, and Super High Frequency SHF) [4,5]. (ii) The second advantage is in terms of the multipath effect of optical signals that consume much lower power, as compared to the line of sight (LoS) signal. Both of the above advantages achieve the goal of this work by providing reasonably higher localization accuracy. Currently, most of indoor applications are Wi-Fi-enabled. In the literature, it is the most studied technology used for indoor localization [6, 7, 8, 9]. Horus is a WiFi-based localization software system [10] that relies on the fingerprinting technique. Moreover, authors in [10] have proposed a deep learning technique for training the data. In [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], authors have applied different localization techniques, including channel state information-based localization (CSI) [12, 13], fingerprint-based localization [14, 15, 16, 17, 18], and additionally localization based upon angle of arrival (AoA), time of flight (ToF), and time difference of arrival (TDoA) [19, 20, 21, 22]. All of these techniques are prone to multipath fading, clock synchronization, and environmental noise. However, existing Wi-Fi networks are efficient in getting the maximum data rate and network coverage rather than for localization purposes; for example, special hardware deployment is needed to read the signals containing localization information. In [22], authors used the received signal strength indicator (RSSI) in the online phase with a Kalman filter to observe the median accuracy. Moreover, the room-level implication was performed in [23] by combining WiFi fingerprinting with a magnetic field, resulting 1.44 m localization error. Other technologies, such as UWB, acoustics, RFID, Bluetooth, and ultrasound, are also implemented in indoor tracking, positioning, and localization. Recently, authors applied UWB with moderate power consumption in [24]; however, it required extra hardware on different user devices. In [25], the authors presented the acoustics-based SITE, combining acoustic Signals and Images to achieve accurate and robust indoor locaTion sErvice, a technique that measures the difference in arrival angles corresponding to the different sources. Radio frequency identification (RFID) combined with pedestrian dead reckoning (PDR) was deployed in [26] to precisely estimate the motion of a user, however this depends on a common unit for reading RF and PDR.