## 3 Navigation
### Sensors
#### 3.1.4. Wi-Fi, UWB, iBeacon, and Radio Frequency Identification  
GPS receivers cannot perform well in indoor environments because of the absence of a line of sight to GPS satellites. Therefore, some other sensors are used in the indoor navigation systems, such as Wi-Fi, UWB, iBeacon and radio frequency identification (RFID). Wi-Fi and UWB are protocol standards for short range wireless communications with low power consumption [36; 37]. Wi-Fi requires access points (APs) in the area in which the target needs to be tracked. By UWB, an efficient indoor navigation system cannot be ensured because of antenna mismatch, low power emission, or possible external interference from other systems. In RFID technology, position accuracy depends on the type of the tags used, which can be either active or passive, as well as the number of tags. Different than passive tags, active tags contain embedded batteries to develop position accuracy, but at high cost [38]. iBeacon is Apple Inc.'s (Cupertino, CA, USA) term used to describe its own implementation of Bluetooth Low Energy (BLE) beacon technology within iOS7. Moreover, the iBeacon protocol can be used in both iOS systems and Android ones, making this new approach portable [39]. Smart devices, such as iPhones or Android phones, can detect advertising signals sent by tiny, low-energy-consumption devices. Notifications will be pushed automatically to the user when a smart device with iBeacon apps enter come to certain areas. iBeacon has been used for the indoor location systems in hospitals [40] and museums [41]. A luggage tracking use case using iBeacon, with a evaluation of its possibilities and restrictions, is included in Ref. [42]. The four kinds of sensors discussed in this subsection all use the received signal strength (RSS) from the APs to estimate the targets' locations. We call this group of methods RSS-indicator (RSSI)-based approaches. The idea underlying RSSI-based approaches is to measure the received signal strength from multiple APs, which is related to the distances between the target and the APs, and to solve the target's position by combining all of the constraints together as a nonlinear least-squares problem. RSSI-based approaches work as follows. The distance \(z_{n}(t_{i})\) between the \(n\)th AP and the target at sampling time \(t_{i}\) can be calculated by the RSSI nominal value \(P_{n}(d,\phi,t_{i})\) as follows:  
\[z_{n}(t_{i})=d_{0}10^{\frac{P_{n}(d_{0})-P_{n}(d,\phi,t_{i})}{100}} \tag{3}\]  
where \(d_{0}\) is the close-in reference distance, \(P_{r}(d_{0})\) is the RSSI in dBm units with the reference distance \(d_{0}\), and \(q\) is the path loss exponent. Let \(x\) and \(y\) be the target location along the horizontal and longitudinal axes. Assume that \(d_{n}(t_{i})\) is the actual distance between the \(n\)th AP and the target at sampling time \(t_{i}\). By the measurement of the APs, it can be found that the actual distance \(d_{n}(t_{i})\) is a function of the state \(x(t_{i})\) as follows:  
\[d_{n}(t_{i})=\sqrt{\left(x(t_{i})-x_{n}(0)\right)^{2}+\left(y(t_{i})-y_{n}(0) \right)^{2}}\]  
where \(x_{n}(0)\) and \(y_{n}(0)\) are the horizontal and longitudinal coordinates of the \(n\)th AP, while \(x(t_{i})\) and \(y(t_{i})\) denote the real location of the target in the 2D tracking space. In general, the measured distance \(z_{n}(t_{i})\) in Equation (3) and the actual distance \(d_{n}(t_{i})\) are not identical. The measurement error should be considered: \(z_{n}(t_{i})=d_{n}(t_{i})+v_{n}(t_{i})\), where \(v_{n}(t_{i})\) is the measurement noise of the \(n\)th AP at the sampling time \(t_{i}\). The measurement covariance of RFID satisfies  
\[v_{n}(t_{i})/d_{n}(t_{i})^{-}N(0,\left(\frac{0.2303\sigma_{p}}{\gamma}\right) ^{2})\]  
where \(\sigma_{p}\) is the standard deviation, \(\gamma\) is the path loss exponent and '/' denotes a division calculation.