# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 8 Conclusions  
Ultra-wideband technology offers attractive features such as its accuracy, robustness, and ability to penetrate obstacles, making it one of the preferred choices for implementing real-time indoor positioning and tracking systems. In this review, we investigated the real-time indoor positioning, tracking, and localization systems developed during the past 5 years. A total of 147 research papers were included this review of real-time positioning systems using different modules, units, and chips from different vendors, with systems implemented using different methods and algorithms. Basic information was extracted from the works included in the survey period, such as the type of hardware used and its origin, methods of implementing the systems, types of sensors and technology used, algorithms and filters to improve performance, and the accuracy of the measurements obtained. This review ensures a clear picture of the types of methods and algorithms used to implement these systems in real time. The selected works were addressed and discussed from several perspectives in terms of the hardware components used, the frequency range and ultra-wideband channels, the types of topologies implemented, the filters and algorithms to improve the performance, and the accuracy of the obtained performance, which we believe are important for any researcher in this field to build an initial idea about how to choose the appropriate type and method to implement this process in real time.  
Conceptualization, M.F.R.A.-O., T.R. and K.T.; formal analysis, M.F.R.A.-O. and K.T.; writing--original draft preparation, M.F.R.A.-O.; methodology, M.F.R.A.-O., S.J. and K.T.; writing--review and editing, M.F.R.A.-O. and K.T.; visualization, M.F.R.A.-O.; supervision, K.T.;  
Figure 18: The percentage range of errors for the included systems.  
project administration, T.R. and K.T. All authors have read and agreed to the published version of the manuscript.  
This research is part of the Synergy Project ADAM (Autonomous Discovery of Advanced Materials), funded by the European Research Council (grant number: 856405).  
Not applicable.  
The authors would like to thank the European Research Council (ERC) for funding the Autonomous Discovery of Advanced Materials (ADAM) project.  
The authors declare no conflicts of interest.  
Adaptive robust particle filtering  
Artificial neural network  
BLE  
Bluetooth Low Energy  
CRLB  
Cramer-Rao lower bounds  
CNN  
Convolutional neural network  
DF  
Debiasing filter  
ECA  
Efficient channel attention  
FNN  
Fuzzy neural network  
GPS  
Global Positioning System
BYOPS  
Bring Your Own Positioning System  
AltDS-TWR  
Alternative double-sided two-way ranging  
SDS-TWR  
Symmetric double-sided two-way ranging  
EDS-TWR  
Extended double-sided two-way ranging  
EFIR-RTS  
Extended finite-impulse response-based Rauch-Tung-Striebel smoother  
DS-TWR  
Double-sided two-way ranging  
DBLD  
Dynamic best link discovery  
DNN  
Deep neural network  
FLA  
Five-line approximate  
IPS  
Indoor positioning system  
ISAR  
Inverse synthetic-aperture radar  
IGN  
Iterative Gauss-Newton  
RF  
Radio frequency  
KF  
Kalman filter  
CKF  
Cubature Kalman filter  
KNN  
k-Nearest neighbors  
LoS  
Line-of-sight  
LS  
Least squares  
LSTM  
Long short-term memory  
LinHPS  
Linear hyperbolic positioning system  
ML  
Machine learning  
MAL  
Multi-algorithm  
MPGA  
Multi-population genetic algorithm  
MCC-VC  
Maximum correntropy criterion with variable center  
MCA  
Multipath component analysis  
NLoS  
Non-line-of-sight  
NN  
Neural network  
PF  
Particle filter  
REKF  
Robust extended Kalman filter  
RFID  
Radio frequency identification  
RSSI  
Received signal strength indicator  
RNN  
Recurrent neural network  
SAR  
Synthetic-aperture radar  
SWF  
Sliding window filtering