This blog post is a duplication from a subsection of my dissertation (Section 2.1.2), that briefly explains the technological perspectives of available Indoor Position Systems (IPS) in literature. For the sake of brevity, while attempting to cover the whole IPS landscape as much as possible, the excerpt for each IPS technology was presented by using a maximum of four sentences omitting the technical details. The cited references in each presented technology gave such specific information. Thus, the technologies for IPS are briefly expressed in an abstract level as follows [1, 2, 3, 4, 5, 6, 7, 8]:

  1. WiFi-based IPS: is the most popular technology in the field of indoor localization because it is the default technology in WLAN as well as its availability in the modern smartphone [9, 10]. WiFi, based on the IEEE 802.11 standard for WLAN, operates on the 2.4 GHz and 5.0 GHz using typical channel bandwidths of 20MHz, 40MHz, and 80MHz. The main applied signal measurement technique is Received Signal Strength (RSS) method (i.e. Channel State Information (CSI) is still in its early stage and Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) are seldom used) and the most applied positioning technique is Fingerprinting method (i.e. Lateration is usable but rare) [11, 9]. The core challenge in WiFi-based IPS is that the radio frequency signals are suffered from several sources of disturbance and alternation in the indoor environment while most of the WiFi-based IPSs rely on the existing infrastructures, which are mainly deployed for communication purposes instead of a localization [5, 12].

  2. Bluetooth-Low-Energy (BLE)-based IPS: is the second most popular technology, after the WiFibased system, in indoor positioning and navigation system. BLE generally operates on 2.4 GHz with 2MHz bandwidths and uses frequency hopping technique for data communication [13, 14]. For signal measurement method, RSS is mostly utilized similar to the WiFi technology and Fingerprinting technique is the most commonly applied location estimation method in BLE-based IPS [15]. In general, the achievable accuracy in BLE is normally higher than the WiFi though the coverage area of BLE is quite small, i.e. usually less than 20m.

  3. Light-based IPS: uses a light source (such as Light Emitting Diode (LED)s for Visible Light Communication (VLC), Infra-Red (IR) LEDs or IR lasers for IR-based system) to transmit data at the transmitter and photo-detector (i.e. for VLC), and IR photo-diode or an IR camera are typically used in the receiver [16]. As a signal measurement technique, RSS, ToA, and TDoA methods are generally used in light-based IPS whereas Lateration (for VLC and IR laser system) and angulation (for IR-camera system) technique are usually applied as the positioning method [17, 18]. Most of the light-based IPSs require LOS situation (i.e., otherwise the accuracy suffers significantly) and they are acknowledged to be in the early stage of development in the field [5, 19, 16].

  4. Computer Vision-based IPS: typically uses the visual odometry technique (i.e., estimating the position of target based on the associated camera images) (or) Vision-based Simultaneous Localization and Mapping (SLAM), (i.e., SLAM is also applicable in laser, sonar, and odometry data other than Vision) to estimate the location of interesting objects within the Field-of-View (FoV) of the applied cameras [20, 21]. There are generally two approaches namely device-based and device-free systems: the former uses markers or printed QR codes to locate the target objects whereas the latter typically utilized the information gathered from several cameras in the environment for location estimation [22, 23]. The main challenges in Vision-based IPS include the occlusion of objects, lighting conditions, computational cost, shadows from other objects, etc [22, 24]. Vision-based localization system has several future potentials in IPS wheres its current applications mainly lie in autonomous driving and visual reality fields [5].

  5. Sound-based IPS: relies on acoustic signal (i.e., based on ultrasound or audible frequencies) to acquire the information necessary for location estimation of objects in indoor environments [25, 26]. In the signal measurement phase, the ToA, TDoA, and RSS techniques are typically applied in sound-based IPS depending on the concrete system setup. Accordingly, Lateration or Fingerprinting techniques are usable as positioning algorithms. In general, Line-of-Sight (LOS) condition is mandatory in sound-based IPS and the accuracy can also be varied due to the changes in temperature and humidity in the environment as those can impact the speed of sound in its communication medium (i.e., air) [5].

  6. Magnetic Field-based IPS: uses the changes in strengths of magnetic fields measured by a measuring device to estimate the position of intended targets [27, 28]. To accomplish this, a database that records several variations of the magnetic field strengths for the intended environment is necessary to be created, and Fingerprinting algorithm can be used to compute specific location estimations of the targets [29, 30]. The strengths of the magnetic field can be affected by moving objects that contain ferromagnetic materials and electronic devices. As a result, the estimated location accuracy can be deteriorated [31].

  7. IMU-based IPS: uses measurement data gathered from multiple sensors (typically three: namely accelerometer, gyroscope, and magnetometer) to estimate the location of the interested object using Dead-Rekoning (DR) and sensor fusion techniques [32, 33]. DR, a.k.a Padestrain Dead-Rekoning (PDR) for pedestrian target, is a method that estimates the current position of a moving target based on the previously known location (usually called as a fix) and the estimate of its incorporated measurements from the mentioned three sensors regarding the motion of the target [34, 35]. IMU-based IPS commonly suffers from cumulative errors, but can be reduced if the current estimation is not too far away from the last known fix [36].

  8. Ultra-Wideband (UWB)-based IPS: typically uses impulse radio technology [37] for location estimation, in which the technology inherently possess properties such as large bandwidth, high time resolution, and low power consumption due to a very short pulse, robustness against multi-path, ability to penetrate walls, high data rate due to large bandwidth, etc [38, 39]. For the signal measurement technique, the most commonly applied methods in UWB-based IPS are ToA and TDoA (i.e., AoA and RSS are seldom used due to higher complexity and poorer performance issue compared to the other two methods) whereas the Lateration method is the common choice for its positioning algorithm [40, 41, 42]. The coverage area (i.e., typically less than 60m) and the cost were usually reported as the main hurdle in UWB system. However, it is expected that the recent incorporation of the UWB chip into smartphones by Apple and Samsung will further boost the technical progress and system use-case of UWB in IPS.

  9. Other Technologies in IPS: do exist and their contributions to the field are enormously important though they were seldom addressed in detail in the literature. It is expected that new technologies specifically for IPS may still emerge in the near future and the list is, by no means, complete, rather the most commonly used ones. For brevity, the rest of technologies applicable in IPS can be named as: Radio Frequency Identification (RFID)-based IPS and near field communication based IPS [43], positioning algorithms built upon the 5G or 6G cellular network [44], radio technology such as Zigbee and Radar [45, 46], pseudolite system which is designed to produce GPS signal into its deprived areas [47], hybrid systems (a.k.a signal of opportunity approach) that attempt to combine several technologies into one common framework [46], etc.

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