# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 2 Related Work  
Support for a variety of location technologies to provide reliable and accurate user location has been a key research area of the 3 GPP standard. The authors of [11], introduce a downlink angle of departure (DL-AoD) and an uplink angle of arrival (UL-AoA), multi-round trip time (Multi-RTT), downlink time difference of arrival (DL-TDOA), uplink time difference of arrival (UL-TDOA), and other new positioning measurement methods. It greatly expands the positioning mode supported by the 5G system and makes up for the deficiency that R15 does not support positioning using NR signals and improves the performance of UE positioning, especially in indoor environments where GNSS cannot work normally. In R16, the used reference signal for positioning are PRS and SRS-Pos for downlink measurement and uplink measurement, respectively. The main principle of TDOA is to calculate the UE position by constructing a nonlinear hyperbolic equation, wherein a distance difference between every two base stations to a single UE is modeled according to the signal arrival time difference. There are many kinds of literature to investigate various methods to obtain signal arrival time difference for TDOA localization, such as the cross-correlation algorithm using received signal and transmitted signal [12], based on timing advance (TA) value to obtain the signal arrival time difference [13], etc. However, one disadvantage of these methods is that the phase characteristics of the system cannot be obtained, and it is sensitive to additional signals. AoA is a positioning algorithm based on the angle of arrival of signals. UE positioning is carried out by measuring the azimuth angle of arrival (A-AoA) and the zenith angle of arrival (Z-AoA), which can be configured on the network side [11]. The proposed positioning methods in [14; 15] were based on MIMO (multi input multi output) technology and greatly improve the positioning accuracy. In [16], the author studies the characteristics of time-frequency signals and uses the orthogonality of signal and noise subspace to obtain more accurate angle estimation results. Once the signal arrives, some references provide specific methods for calculating UE coordinates from angle estimates. For example, in [17] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Release** & **Bandwidth** & **Positioning Accuracy** \\ \hline R16 & 100 MHz & 1–3 m \\ R17 & 100 MHz & 0.5 m \\ R18 & 200–800 MHz & 10 cm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Positioning accuracy requirement in NR System. the author introduces the modified polar representation to calculate the localization of a cue using AoA, and in [18], the author proposes a closed-form solution for 3D localization using AoAs. For such TDOA- or AoA-only positioning methods, there are some disadvantages. The positioning accuracy of AoA is very dependent on the angle measurement accuracy of the array antenna and is sensitive to signal propagation distance. TDOA has high positioning accuracy, but is seriously affected by channel uncertainty. Therefore, a mixture of multiple positioning methods can be considered. In the existing studies, the fusion of localization schemes mainly includes TDOA + AoA and TDOA/AoA + fingerprints [19; 20; 21; 22; 23]. The combination of TDOA and AoA can better solve the synchronization problem in a time-based positioning scheme and the problem that the angle-based positioning scheme is sensitive to the rotation angle of the device. In the scenario of IIoT, higher requirements are put forward for positioning accuracy and power consumption of positioning equipment. In [24], the author proposes a big-data-driven method to improve positioning accuracy by analyzing the data of low-power beacons and scanners to detect the indoor position of users in a specific \"activity-based area\" during the activities of daily life. However, such a method requires higher signal processing capability on the sensors.