Figure 37.4 Positioning based on time difference of arrival (TDoA) [27].
Source: Reproduced with permission of IEEE.
RToF techniques measure the time of flight of a signal from the transmitter to the measuring unit and back. While ToA techniques calculate delay by using two local clocks in two different measuring nodes, RToF techniques use only one node to record the transmitting and arrival times. Thus, RToF techniques are less susceptible to synchronization problems than the other time‐based methods. An algorithm to measure the RToF of Wi‐Fi packets is presented in [9], with the results indicating measurement errors of a few meters. The positioning algorithms for ToA can often be directly applied in RToF techniques. Typically, the mobile subject responds to a received signal from the measuring units, and these measuring units calculate the RToF; however, it is difficult for the measuring unit to know the exact processing/response delay time at the mobile subject. Another challenge is that the measuring node may become overloaded when tracking multiple mobile subjects moving quickly. The 3D‐ID system [50] uses the RToF for distance estimation during localization. In the proposed approach, whenever a mobile tag receives a broadcast, the tag immediately rebroadcasts it on a different frequency, modulated with the tag’s ID. A cell controller cycles through the antennas, collecting a set of ranges to the tag. With a 40 MHz signal, this system was shown to achieve a 30 m range, 1 m precision, and 5 s location update rate.
It should be noted that radar (RAdio Detection And Ranging) systems exploit time‐based methods, such as the ones discussed above, for localization of an object. The original principle of radar was to measure the propagation time and direction of radio pulses transmitted by an antenna and then bounced back from a distant passive target. If the object returns some of the wave's energy to the antenna, the radar can measure the elapsed time (i.e. RToF) to estimate the distance, as well as the angle of incidence (by using a directional antenna). This original concept of radar assumes passive object reflection and involves only one station with both a transmitter and sensor. But in such systems, most of the signal energy gets lost due to reflection, and the use of steerable directional antennas is impracticable. Therefore, the concept of radar has been extended to include more than one active transmitter (secondary radar). Instead of passive reflection, the single‐way travel time of the radar pulse is measured by ToA and then returned actively. Frequency‐Modulated Continuous Wave (FMCW) radar is a short‐range localization technique, where the transmitter frequency is linearly increased with the time [1]. The returned echo is received with a constant offset, which relates to the distance traveled. An advantage of FMCW is its resistance to the Doppler effect. The Doppler movement introduces a shift in the frequency which is canceled out by differencing. Most FMCW radar implementations make use of multilateration based on RToF‐based distance estimates between a mobile transmitter and multiple fixed transponders. The transponder broadcasts a radio signal in the free ISM band (5.725 GHz to 5.875 GHz) which is received, processed, and echoed back to the transponder by each transmitter without time delay. The echo is coded with the respective transponder’s identification in order to allow the transmitter to separate each transponder´s answer. A localization system based on FMCW radar was proposed in [51] that consists of multiple fixed base stations and a lightweight mobile transponder operating at 5.8 GHz. Based on TDoA ranges at centimeter‐level precision measured under LOS conditions, a positioning accuracy of 10 cm over 500 m was shown to be achieved.
37.5.1.3 Signal‐Property‐Based Methods
The triangulation‐based localization techniques discussed previously compute the distance to the mobile subject using either timing or angle information. But in the absence of LOS channels between transmitters and receivers, the underlying mechanism in both types of techniques (time and angle) are impacted by multipath effects, which can reduce the accuracy of the estimated location.
An alternative approach to measuring the distance of a mobile subject to some reference measuring nodes involves using the attenuation of the emitted (radio) signal strength. Theoretical and empirical models are usually used to translate the difference between the transmitted signal strength and the RSS into a range estimate. Such an RSSI is the most widely used signal‐related feature [52]. Typically, RSSI measurement estimations depend heavily on the environment, and are also nonlinear. Several techniques make use of RSSI with Wi‐Fi technology for indoor localization. As path loss models that are essential for such techniques are also impacted by multipath fading and shadowing effects [27], often indoor site‐specific parameters need to be used for these models. Some efforts have been proposed to improve accuracy in such cases; for example, [53] uses pre‐measured RSSI contours centered at the receiver to improve localization accuracy with cellular network signals, while [54] employs a fuzzy logic algorithm to improve Wi‐Fi RSSI‐based localization. In [55, 56], Bluetooth RSS was used to estimate distances and then an extended Kalman filter (EKF) algorithm was applied to obtain 3D position estimates.
Another approach to estimating distance is to use the signal phase (or phase difference) property [57]. As an example, assuming that all transmitting stations emit pure sinusoidal signals that are of the same frequency, with zero phase offset; then the receiver can measure the phase difference between the signals transmitted by the stations, which is a function of its location with respect to the stations. It is possible to use the signal phase approach together with ToA/TDoA or RSSI techniques to fine‐tune the location positioning. However, the signal phase approach is susceptible to interference along NLOS paths that can introduce errors.
37.5.2 Fingerprinting
Fingerprinting techniques refer to algorithms that estimate the location of a person or object at any time by matching real‐time signal measurements with unique location‐specific “signatures” of signals (e.g. Wi‐Fi RSSI). Typically, fingerprinting can be performed analytically or empirically.
Analytical fingerprinting, for example, RSSI‐based, involves using propagation models such as the radial symmetric free‐space path loss model to derive the distance between a radiating source and a receiver by exploiting the attenuation of RSSI with distance. Unfortunately, this simplistic model is rarely applicable in indoor environments, where the signals do not attenuate predictably with the distance due to shadowing, reflection, refraction, and absorption by the indoor building structures. Therefore, other models have been proposed, such as the Indoor Path Loss Model [58] and the Dominant Path Model [59], which takes into account only the strongest path, which is not necessarily identical to the direct path.
Empirical fingerprinting is more commonly used in various indoor localization techniques due to the difficulty in analytically modeling unpredictable multipath effects. There are typically two stages involved in such empirical location fingerprinting: an offline (calibration) stage and an online (run‐time) stage. The offline stage involves a site survey in an indoor environment, to collect the location coordinates/landmarks/labels and strengths (or other features) of signals of interest at each location. This procedure of site survey is time consuming and labor intensive. However, such a survey can account for static multipath effects much more easily than with analytical fingerprinting (although dynamic effects, e.g. due to different number of moving people are still problematic and can cause variations in readings for the same location). Several public Wi‐Fi APs (and also cellular network ID) databases are readily available [60–63] that can somewhat reduce survey overheads for empirical‐fingerprinting‐based indoor localization solutions; however, the limited quantity and granularity of fingerprint data for building interiors remains a challenge. In the run‐time stage, the localization technique uses the currently observed signal features and previously collected information to figure out an estimated location, with the underlying premise that