# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 5 Real-Time UWB Indoor Positioning Systems (RTIPSs)
### Indoor Positioning Systems for Unmanned Aerial Vehicle Applications  
The use of tracking systems for unmanned aerial vehicles is an essential element. This is due to the nature of the tasks that they perform through flight and their specific applications, which are often in open outdoor environments outside buildings and usually use satellite navigation and tracking systems. The need to use these vehicles in indoor environments for tasks that mobile robots cannot perform is a challenge for them, as satellite signals are weak and unusable. Here, the need to use indoor tracking and positioning systems of various types, especially those operating on ultra-wideband waves, arises. The current review paper is limited to indoor tracking and positioning systems, so this chapter will be limited to works that meet this description. [PERSON] et al. [136] presented an indoor navigation system for UAVs for fusing data from UWB, IMUs, and barometers using an error-state unscented Kalman filter. The Crazyflie 2.1 (Bitcraze AB, Malmo, Sweden) open-source platform and the Loco Positioning System (Bitcraze AB, Malmo, Sweden), which is based on the DecaWave DWM1000, were used for system implementation. The developed approach was implemented on the UAV MCU and tested in real-time closed-loop flight. [PERSON] et al. [137] proposed an indoor RTLS for the localization of multiple UAVs using maximum likelihood Kalman filtering (MLKF). This work aimed to enhance the localization performance for flying objects in the vertical axis via the anchor's height level similarity. The work used Flame Wheel 450 (DJI Technology Inc., Shenzhen, China) UAVs with a wheelbase of 450 mm, equipped with an onboard Intel CPU (Intel Corporation, Santa Clara, CA, USA) for running the Ubuntu16 (Canonical Ltd., London, UK) operating system. Four UWB anchors were distributed in four corners of the testing environments and tested with several heights, from 90 cm to 180 cm. The used UWB system operated on channel 1, with 3.5 GHz central frequency and a 110 Kbps sample rate. High-precision motion-capture cameras were used for providing true position and speed. The practical system tests showed that the localization error was reduced by 60% by using the proposed MLKF, with only 20% of the computational time of the classical method. [PERSON] et al. [138] proposed a precise energy-saving indoor RTTS for UAVs in coal mines using an on-demand trigger algorithm based on an unscented Kalman filter. The filter fuses the data from UWB units and IMU units to achieve on-demand precise tracking (OPT). The P440 UWB module (TDSR LLC., Petersburg, TN, USA) was used to implement the anchors and tag. The Amovlab Z410-4b (Amu Lab-Chengdu Bobei Technology Co., Ltd., Chengdu, China) UAV was used for system implementation and testing, where the UWB tag was fixed on the UAV chassis. The practical tests were implemented in indoor laboratories as well as in coal mine indoor environments. The test results revealed that the proposed method improved the accuracy by 10.3% and 11.4% in indoor laboratory and coal mine environments, respectively, and the energy consumption was reduced by 19.6%. [PERSON] et al. [139] proposed an indoor UAV formation system based on a long short-term memory (LSTM) neural network. The LSTM network combined with row data of UWB units was used for UAV positioning using historical information for enhancing the system performance. Five Firefighter UAVs (Yifei Intelligent Control Technology Co., Ltd., Tianjin, China) were used to hold the tags of the LinkTrack-P (SZ Nooploop Technology Co., Ltd., Shenzhen, China) positioning system, which also includes four anchors as a base station for practical system testing in indoor environments. The test results indicated that the system achieved a positioning accuracy of 5 cm. [PERSON] et al. [140] proposed a UWB-incorporated visual-inertial odometry (VIO) system for UAV indoor navigation in GNSS-denied environments.