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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.