Nowadays, smartphones have become one of the most common technologies in everyday society and they are mostly used indoors. Ref. [40] states that “80% of smartphone usage happens inside buildings.” The majority of modern smart devices are WiFi capable, making WiFi a great choice for indoor localization as well as one of the most thoroughly researched localization technologies in the literature. Because existing Wi-Fi access points may also be utilized as signal collection reference points, modest localization systems (with reasonable localization accuracy) can be created without the requirement for additional infrastructure [2]. Wi-Fi positioning systems have been in the lead for commercialized indoor localization, due to the massive deployment of Wi-Fi access points by mobile network carriers. Unfortunately, WPSs majorly depend on the density and distribution of Wi-Fi access points (APs) in the known environment, which directly affects the accuracy and the availability of the systems. Unfortunately, WPS accuracy and availability degrades as a result of its reliance on the number and distribution of Wi-Fi APs in its unique indoor service region. Although unsupervised as well as supervised Wi-Fi APs have been used to improve the location databases (DBs), such as fingerprinting DB or AP location DB, to increase the localization performance, taking environmental factors into account has little or no effect on improving location effectiveness in Wi-Fi dead zones. While the installation of additional APs will improve the system performance, the mobile network carriers usually are not willing, as they make the systems less time-efficient and more costly [74]. As mentioned previously in the paper, Wi-Fi is also the technology used for fingerprinting approaches such as RSS, CSI and FTM (see Section 2.2.1, Section 2.2.2 and Section 2.2.4).

Ref. [74] proposes and implements a highly scalable 3D indoor positioning system based on loosely linked Wi-Fi/Sensor integration. Location database, which is derived using dynamic surveying data, is used to estimate Wi-Fi location. PDR is utilized as a time update model to compensate for the limitations of pedestrian motion modeling. The test findings suggest that providing a stable and accurate 3D indoor location in a scaled indoor environment is doable by using the basic yet complimentary loosely coupled Kalman filtering.

The researchers in [36] propose a robust 3D indoor positioning system appropriate for an indoor IoT application. This system is based on a Bayesian network that operates by determining the intensity of Wi-Fi signals. Using just four APs and a modest number of RPs, the suggested 3D Bayesian Graphical Model (3D-BGM) obtained an overall localization accuracy of 2.9 m.

WiFi round-trip time (RTT) was utilized in [90] for a 3D indoor localization algorithm for smartphones. In the proposed algorithm, the weighted centroid (WC) algorithm is utilized to estimate the rough two-dimensional (2D) position due to its easy implementation and low complexity. The coarse target altitude is acquired according to pedestrian activity. Then, the coarse altitude and 2D position combine into a rough 3D position, which is regarded as the initial position of the standard particle swarm optimization (SPSO) algorithm. The SPSO algorithm aims to estimate a more accurate 3D location on the basis of the cursory 3D position of the smartphone. To reduce computation, the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to assist in updating the SPSO particles. Experimental results show that the proposed positioning algorithm has better 3D accuracy than WC and least-squares (LS) algorithms, with a 2D accuracy of 1.147 m and an altitude precision of 0.305 m.

In [91], a smartphone-based 3D indoor positioning method is proposed which takes into account information from a WiFi interface and from the barometer sensor. Several experiments have been performed in two real scenarios and measurements have been made over commercial mobile devices. When tested in two different environments, it was distinguished that this method allows obtaining a lower positioning error even if few APs are available: when more than five Access Points (APs) are used, the proposed 3D positioning system is able to accurately localize the user with an error below 2 m and 1.2 m, respectively.

Bluetooth was established as an open specification with low power, short range wireless data and voice connections and has long been used in the communication and proximity markets. It is used to transmit data over short ranges between devices via ultra high frequency (UHF) radio waves, ranging from 2.402 GHz to 2.48 GHz. Initially, it was developed as a wireless replacement for the RS-232 data cable. Similarly to WiFi, due to its broad availability in smart devices, it also seems like a great option for indoor localization. There are currently two main types of Bluetooth indoor positioning solutions: connection-based and inquiry-based [92].

While Bluetooth Low Energy (BLE) may be utilized with many localization approaches such as RSS, AoA and ToF, the majority of existing BLE-based localization solutions rely on RSS-based inputs since RSS-based systems are believed to be much simpler. However, due to the fact that it is strongly dependent on RSS-based inputs, the localization accuracy is limited. Despite the fact that BLE in its original form can be used for localization (due to its range, low cost and energy consumption), two BLE-based protocols, iBeacons (by Apple Inc., California, U.S.) and Eddystone (by Google Inc., California, U.S.), have recently been proposed, primarily for context aware proximity-based services [2].

The research in [92] presents an inquiry-based Bluetooth indoor positioning method using RSS probability distributions. The results suggest that the RSS probabilistic technique is a viable option for Bluetooth positioning. On the other hand, Bluetooth positioning has a substantial bottleneck owing to the low power consumption protocol: the updating frequency. Considering the accuracy of position determination is not very high, the test results show that the technique suggested in this study performs rather well. When compared to WLAN positioning, however, the Bluetooth signal characteristics and the number of access points result in lower accuracy.

The authors in [93] discuss low-cost 3D indoor positioning with Bluetooth smart device and least square (LS) methods. Nonlinear least square (NLS) method is adopted for parameter estimation of Bluetooth signal propagation model and various linear least square methods are used for 3D location estimation of the target Bluetooth device. Simulation and hardware experiment results illustrate that the nonlinear least square method is suitable for parameter estimation of Bluetooth signal propagation and the generalized least square (GLS) method has better performance than total least square methods. The proposed method also has the merits of low cost, low power consumption, high usability and high location precision. The hardware experiments have achieved a 3D positioning accuracy of 2.27 m and this was lowered to 1.97 m when combined with a barometer.

In cellular-based localization, downlink transmissions from the Base Station (BS) to the mobile device and uplink transmissions from the mobile device to the BS can be used to facilitate user positioning. The cellular-positioning techniques can be divided into two types based on the entity that computes the position: (1) mobile-based, in which the user device calculates its own location, and (2) network-based, in which the network location server computes the user device’s position. Most cellular-based positioning systems are network-based due to their centralized design, which provides the network operator complete control of the location service, as well as their support for older devices. After an extensive literature review, no relevant 3D positioning works have been identified utilizing 2G–4G cellular technology. This is mainly due to the fact that at the time that these technologies were developed the need for 3D positioning was not as high as it is now. Therefore, the majority of existing systems using cellular technology are 5G-based. Ref. [94] suggested a 3D positioning method in a simulated indoor 5G ultra-dense network. The paper suggests a 3D dynamic reconstruction fingerprint matching technique, with the first step being to rebuild the entire fingerprint matrix from partial data. The sub-optimal service base stations are then removed from the dataset to simplify the fingerprint data. Finally, the 3D coordinates are estimated using the k-nearest neighbor matching approach. Positioning errors are assessed at various Signal-to-Noise Ratio (SNR) levels. The mean error is 0.31 m at SNR = 2 dB and 0.16 m at SNR = 20 dB. Ref. [95] focused their research on positioning a single cell (base station) equipped with a wideband 5G signal and a vector antenna (VA). This technique avoids the problems of multi-cell systems, such as base station synchronization and greater deployment costs due to system complexity. They employed statistics-based expectation maximization and the subspace-spaced technique to estimate position. The results which were obtained using sounding reference signals in a line of sight scenario demonstrate that VA is capable of providing 3D positioning with sub-meter accuracy in 5G networks without the need for numerous cells or antennas. The researchers in [96] discuss various ways of utilizing space detection to achieve more accurate and precise results for indoor localization. The designed and developed 5G simulation as well as the 5G-based particle filter fusion resulted in a reliable localization performance. For this, two approaches were proposed, the first one being the map data out of computer-aided design (CAD) plans and the second the accuracy clarification of the positioning technique performance followed by a simple 5G-based PF which uses map information and geospatial analysis, smartphone sensor values and 5G simulation as input to provide a 3D trajectory for a long term robust performance in both online and offline environments. The results of this investigation show that map and routing graph preparation can be carried out efficiently, which ensures the accuracy and precision of indoor localization. The approaches in map generation, simulation and localization were developed using available data sources as well as common algorithms with new usages in the 5G-based fusion domain. Moreover, a novel interpretation in accuracy and precision analyses has been discussed and tested with the simulated 5G measurements, based on the desired 3GPP standards. For a complex building design, errors below 3 m can be considered as the target accuracy of the 5G campus network. In the 4G era, cellular positioning was used for emergency services and services associated with lawful interception. Commercial use cases have gained significant interest concerning 5G and use cases such as factory automation, transportation and logistics are included in 5G alongside regulatory use cases. Positioning and location services are expected to be a critical components of the system requested by most commercial applications, such as AR/VR/XR, gaming, sensing, low-cost tracking and new industrial applications requiring exceptionally high precision as we move closer to 6G. This could also be enhanced by fusing with artificial intelligence powered mobile networks as suggested in [97]. As a result, location accuracy and latency requirements are expected to tighten even more with 5G [98]. The fifth generation (5G) new radio (NR) had a successful worldwide release in 2020. After a few years, the majority of the world has already adapted to this new communication standard and there is now a need to aim for new potential technologies while finding substantial use cases for the next generation of wireless systems, termed 6G communication systems. Wireless networks are frequently praised only for their communication capabilities, while their inherent positioning and sensing benefits are disregarded. In this sense, the 5G NR access interface, with its high carrier frequency, large bandwidth and massive antenna array, provides excellent prospects for precise localization and sensing systems. Furthermore, 6G systems will accelerate the transition to even higher frequency operation, such as millimeter wave (mmWave) and THz ranges, as well as significantly wider bandwidths. Furthermore, the THz frequency range provides several opportunities, including not just precise localization but also high-quality imaging and frequency spectroscopy [17]. In the 5G evolution to 6G, connectivity remains one of the most significant enablers of new services, but monetization of private networks requires more than simply a wireless connection. Beyond connectivity, for example, in industrial automation, high-accuracy positioning and sensing must be smoothly integrated into a single communication system [99]. 6G systems built for communication, sensing and location will enable new applications while improving traditional connectivity [98,100]. Future trends in wireless communication indicate that 6G radios are likely to use signals at the mmWave range and have channel bandwidths which are at least five times wider than 5G. From a localization and sensing perspective this has multiple benefits: (1) there is a more direct relation between the propagation paths and the environment as the signals on these frequencies do not typically penetrate walls; (2) the very fine time resolution of the power delay in these wide channels facilitates the resolvability of multi-path components and especially the LoS ones to more accurately estimate ranges; (3) smaller wavelengths that mean smaller antennas, especially phased array antennas that facilitate the good estimation of azimuth and elevation angles and hence enable accurate 3D positioning [17]. In addition to these, the high frequencies to be used in 6G systems open up a new potential in terms of sensing and imaging based on the radar-like technology that arises. The fact that multi-path components are highly resolvable in terms of time, angle and Doppler in the the power delay profile or impulse response enables the acquisition of spatial knowledge about the physical environment (known as imaging). The availability of this environment spatial information will better facilitate the use of Simultaneous Localization and Mapping (SLAM) approaches.

Ultra-wideband (UWB) is a short-range wireless technology which uses much wider bandwidths compared to the narrow-band transmissions typically used in Wi-Fi systems. UWB systems typically use frequencies ranging from 3.1 to 10.6 GHz but the bandwidth needs to be at least 20% of the central frequency. In addition, instead of measuring the signal strengths (RSS), the positioning is achieved by using the transit time methodology (ToA). The advantage of UWB technology compared to other Radio Access Technologies is that it offers “spatial awareness” since the wide bandwidth allows for better resolution in the time domain allowing for more accurate time and thereafter distance estimates to be measured. The localization accuracy could reach a centimeter level of approximately 10–30 cm, in comparison to GPS (1–3 m) or Wi-Fi (2–10 m) [101]. However, the issue with using UWB is that it is extremely short-ranged and requires a direct line of sight between receiver and transmitter due to high losses experienced when signals propagate through obstructions. This requires a greater number of transmitters within an indoor environment, which subsequently increases the cost. Even though it is not as widespread or cost-efficient as other RATs, utilizing the “spatial awareness” of this technology and especially combining it with the cooperative positioning approach, makes UWB a technology to consider in the future. The world’s largest smartphone manufacturers, such as Apple, Samsung and Huawei, are all currently capitalizing on the UWB projects, specifically the manufacturing of the chips and antennas. However, Apple is the first to actually deploy it in a phone, with the others expecting to shortly follow.

In recent years, UWB technology has received a lot of interest for indoor positioning. Several systems have already been implemented commercially, while many others are being utilized as experimental testbeds such as those provided by Decawave and Bespoon companies. These systems have been thoroughly researched and validated for specific purposes. Other activities have focused on modelling the LOS and NLOS circumstances in order to develop NLOS identification metrics that will allow some NLOS mitigation methods to be implemented. The NLOS problem, which is the primary source of inaccuracy in UWB range and positioning, is still an open research topic [70].

Ref. [102] proposes a UWB positioning system which utilizes two way time of flight (TWTF) to compute range measurements. These readings are employed in the multilateration approach to determine the trans-receiver location (TAG). The authors of this paper state that this type of system has the advantage of providing high accuracy positioning (about 10 cm from the state of the art), as well as low power consumption, high multipath resolution, high data rate and other benefits. The system’s testing has statically analyzed the system’s positioning and range capabilities in an indoor office environment. The test yielded an average 3D accuracy of 100 ± 25 mm.

The authors in [103] propose a 3D ToA positioning algorithm while utilizing the UWB technology. The main idea of the proposed algorithm is to replace the quadratic term in the positioning estimation with a new variable and the usage of the weighted least squares linear estimation followed by the combination with Kalman filter to reduce the interference error in the transmission process. The simulation results show that the positioning accuracy can reach about 5–10 cm.

Another example is proposed in [104], where a high resolution UWB positioning radar system based on TDoA was developed. The UWB radar system provides millimeter accuracy in dense multipath indoor environments for 1D, 2D and 3D localization. The system is fully compliant with the FCC UWB regulations and utilizes time domain measurements to suppress both multipath signals and NLOS errors and has a potential for even sub-mm accuracy. Specifically, a 3 mm maximum error was achieved for the x, y dimensions with a 7 mm maximum error in the z-dimension.

The authors in [105] present a novel approach to a self-localizing anchor-system calibration that uses a calibration unit (CU) for improved localization accuracy. This study confirmed that the use of the CU decreases the average positional error of the anchors in 3D UWB localization systems. In addition, the simulations were confirmed to be a valid tool for determining the best position of the CU. Finally, the first demonstration of an anchor calibration with a CU and anchors localized in the working coordinate system in 3D was presented. It had an error of 0.32 m.

Mobile laser scanning (MLS) has been widely used in 3D city modelling data collection, such as Google cars for Google Map/Earth. Building Information Modelling (BIM) has recently emerged and become prominent. Three-dimensional models of buildings are essential for BIM. Static laser scanning is usually used to generate 3D models for BIM, but this method is inefficient if a building is very large or it has many turns and narrow corridors. Therefore, the researchers in [106] propose using MLS for BIM 3D data collection. The positions and attitudes of the mobile laser scanner are important for the correct georeferencing of the 3D models. This paper proposes using three high-precision ultra-wide band (UWB) tags to determine the positions and attitudes of the mobile laser scanner. The accuracy of UWB-based MLS 3D models is assessed by comparing the coordinates of target points, as measured by static laser scanning and a total station survey. The UWB system can achieve centimeter positioning accuracy on the horizontal plane (around 8 cm), but decimeter accuracy in height (around 19 cm).

Millimeter-wave (mmWave) technology is defining a new era in wireless communication by providing very wide bandwidths. This technology is currently used in some Wi-Fi systems (e.g., IEEE802.11ad) and is planned to be used in 5G communications in the near future as it offers much more flexibility to use wider bandwidths and hence have the strong potential to achieve much higher data rates and capacity. mmWave communication systems typically operate in the frequency range between 30 and 300 GHz. The first standardized consumer radios were in the 60 GHz unlicensed band, i.e., 57–64 GHz, where 2 GHz signal bandwidth is typical in applications. The very large availability of bandwidth, together with the use of massive phase array antennas that allow the estimation of the phase can be used for achieving cm-level accuracy or better [18]. Additionally, mmWave systems have higher transmit power allowance compared to UWB systems which compensates partly the high path losses that are typically experienced on those very high frequencies. Another way to alleviate those loss is by using beamforming. Directional beamforming is a challenging task as it requires good knowledge of the propagating channel and also imposes an extra difficulty and challenge in mmWave-based positioning as the exact orientation (azimuth, elevation) angle of the user equipment (UE) should be well known. In [19], the authors derived theoretically the Cramér–Rao bound (CRB) on position and rotation angle estimation uncertainty from mm-wave signals from a single transmitter, in the presence of scatterers. They demonstrated that in open Line of Sight (LoS) conditions, it is possible to estimate the target’s position and orientation angle by exploiting the information coming from the multipath, though with a significant performance penalty. Moreover, the authors of [20] demonstrated the benefits of array antennas towards identifying the orientation of the device. Finally, due to this high sensitivity of the mmWave technology, positioning accuracy seems to be strongly correlated with the distance away from the target to be positioned. For instance, the authors of [23] conducted AoA and signal measurements in a 35 m by 65.5 m open space and achieved a position accuracy ranging from 16 cm to 3.25 m. Positioning research using this mmWave technology is still in very early stages but early theoretical findings and some practical experiments demonstrate its strong potential to achieve the very high accuracy required by modern smart applications. The authors in [107] propose a multipath-assisted localization (MAL) model based on millimeter-wave radar to achieve the localization of indoor electronic devices. The model fully considers the help of the multipath effect when describing the characteristics of the reflected signal and precisely locates the target position by using the MAL area formed by the reflected signal. At the same time, for the situation where the radar in the traditional Single-Input Single-Output (SISO) mode cannot obtain the 3D spatial position information of the target, the advantage of the MAL model is that the 3D information of the target can be obtained after the mining process of the multipath effect. Experiments show that the proposed MAL model enables the millimeter-wave multipath positioning model to achieve a 3D positioning error within 15 cm. A virtualized indoor office scenario with only one mmWave base station (BS) is considered in [108]. User equipment (UE) motion feature, mmWave line of sight (LoS) and first order reflection paths’ AoA-ToA are fused for indoor positioning. Firstly, an improved least mean square (LMS) algorithm that combines motion message is proposed to refine the multi-path AoA estimation. Furthermore, a modified multi-path unscented Kalman filter (UKF) is proposed to track UE’s position in the scenario. The information exchanges of the two stages not only consists of estimates (position, AoA) but also variance of position. Based on the simulation results, the proposed methods provide two times LoS-AoA estimation gains and centimeter 3D positioning accuracy, respectively, of around 60 cm. In addition, this strategy is capable of positioning task with insufficient anchor nodes (ANs).

Indoor localization based on visible light communication (VLC) has gained a lot of attention in recent years. One of its main advantages is its ability to provide high-accuracy positioning by utilizing the ubiquitous LED lights found in modern buildings without the need for any additional specialized infrastructure for location services [68]. According to the optical receiver in use, VLC-based positioning methods in the literature may be divided into two types, camera-based [109] and photodiode-based [110]. Camera-based solutions in particular have proven popular with both academics and industry, for example, because of the high positioning precision achieved by imaging geometry and the strong interoperability of user devices. On a standard smartphone with a front-facing camera, state-of-the-art commercial solutions may provide centimeter-level precision. Despite already existing systems’ promising performance, there are still several practical challenges to be solved [68].

A large majority of VLP solutions rely on multilateration or triangulation to obtain location estimations. However, because of the physical field-of-view limits of both the luminaire (transmitter) and photodiode (receiver) in 3D, performance qualifications of these approaches in 3D positioning are limited and often unattainable. The limitations of FOV have an influence on line of sight (LOS) access to luminaires, which is problematic when several luminaires are required for positioning. Recently, several researchers have been trying to enhance lighting with other peripherals such as more PDs, a steerable laser and even a rotating receiver to eliminate the requirement to position with more than one luminaire while still enabling 3D positioning in the most recent literature. These additional peripherals improve positioning accuracy, especially if they have angular diversity. The developers in [111] introduce the notion of Ray-Surface Positioning (RSP). This method combines angular information from a steerable beam with range information obtained from an isointense envelope measured at a receiver. The first implementation of RSP is discussed to test theoretical and simulated predictions on 3D positioning accuracy and was averaged at around 30 cm.

The authors of [112] describe an RSS-based VLP as a “possible competitor” to UWB-positioning. The paper also describes some approaches already developed by other researchers; for example, in [113], a three-dimensional VLP approach is proposed which is based on Artificial Neural Networks (ANN) utilizing the hybrid between phase PDoA and RSS approach. The approach is believed to minimize the distortions caused by inaccurate modeling as well as improve the overall robustness of conventional VLP systems. In [114], an LED-based 3D IPS is proposed which is aimed at both lighting and communications. The system is based on experimentally measured RSS with less than 3 cm of error. Another efficient 3D VLP algorithm is [115], with the intention of utilizing it for drone navigation. The receiver module did not require any extra height sensors; therefore, a four-LED arrangement was studied. However, simulations revealed that a traditional design of four Light Emitting Diodes (LEDs) arranged in a square form is incapable of solving the 3D position properly achieving accuracy of around 50 cm.

A sound is a mechanical wave-like vibration that propagates or travels across any medium. The medium through which the waves propagate or travel can be either solid, liquid or gas. A sound wave is also the pattern of disturbances caused by the movement of energy away from the source of the sound. Sound waves are sometimes known as longitudinal waves which means the propagation of particle vibration is somewhat parallel to the propagation of energy waves [116]. A source is necessary for the generation of sound. A speaker is an example of a sound source as its diaphragm is able to vibrate in order to produce sound. When a sound source vibrates, the particles in the medium around it vibrate as well. As the medium continues to vibrate because of the vibrating particles, the vibrating particles travel further away from the source of sound. The propagation of vibrating particles away from the source occurs at the speed of sound, therefore creating a sound wave [117].

Sound signals, which are pressure waves moving in the air, benefit from the fact that sound travels at a significantly slower pace than electromagnetic signals, making it much easier to measure the time between signal generation and arrival. Because the radio signal arrives at the sensor almost instantly and the sound signal arrives later, the difference between these two times can be used to calculate distance [118].