Implementing an Industry 4.0 UWB-Based Real-Time Locating System for Optimized Tracking
Abstract
:1. Introduction
2. Literature Review
2.1. Research Methodology
2.2. Related Works
2.3. Ultra-WideBand Technology
2.4. Categorization of Existing Research Efforts
3. Materials and Methods
3.1. Tracking Methods
- Angle of arrival (AoA). The AOA method estimates the position location by means of angular direction observations measured with respect to a reference axis using directional antennas or antenna arrays.
- Time difference of arrival (TDoA). The TDoA method locates a signal source from the different arrival times at the receivers. Once the signal is received at two reference points, the difference in the arrival time can be used to calculate the difference in distances between the target and the two reference points. This difference can be calculated using the equation ∆x = c × (∆t), where c is the speed of light and ∆t is the difference in arrival times at each reference point. To obtain a true Δt measurement, the transmitter and receiver must be synchronized. An implementation based on this technique with unsynchronized devices has been proposed [68], and it was able to calculate the position of the object with an increasing computational cost.
Authors | Tracking Technology | Manuscript Content | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
UWB | BLΕ | Ultrasound | Wi-Fi | IMU | RFID | Visual Tracking | Review | Technology Comparison | System Architecture- Application | |
Jimenez A.R., Seco F., 2016 [51] | √ | √ | ||||||||
Gharat V., et al., 2017 [28] | √ | √ | √ | √ | ||||||
Hyun J., et al, 2019 [65] | √ | √ | ||||||||
Yao L., et al., 2017 [63] | √ | √ | ||||||||
Yadar R., 2018 [32] | √ | √ | √ | √ | ||||||
Athikulwongse K., et al., 2018 [69] | √ | √ | ||||||||
Chen H., et al., 2017 [31] | √ | √ | ||||||||
Jimenez A.R., et al., 2017 [52] | √ | √ | ||||||||
Astafiev A., et al., 2019 [36] | √ | √ | √ | |||||||
Karlsson F., et al., 2015 [40] | √ | √ | ||||||||
Diallo Al., et al., 2019 [29] | √ | √ | ||||||||
Jun Qi et al., 2017 [30] | √ | √ | ||||||||
Duong N. S., et al., 2018 [34] | √ | √ | ||||||||
Jeon J., et al., 2015 [37] | √ | √ | √ | |||||||
Haryanto D., et al., 2018 [41] | √ | √ | ||||||||
Woo S., et al., 2011 [38] | √ | √ | ||||||||
Abbas M., et al., 2019 [39] | √ | |||||||||
Alarifi A., et al., 2016 [70] | √ | √ | ||||||||
Kunhoth J.., et al., 2020 [71] | √ | |||||||||
Yassin A., et al., 2017 [72] | √ | |||||||||
Oesterreich T.D, Osnabrück F.T., 2016 [4] | √ | |||||||||
Kanan R., et al., 2018 [7] | √ | √ | ||||||||
Zhao Z., et al., 2021 [6] | √ | |||||||||
De Angelis G.., et al., 2016 [21] | √ | √ | ||||||||
Ridolfi M., et al., 2018 [18] | √ | √ | ||||||||
Kumler J., et al., 2017 [19] | √ | √ | ||||||||
Yoon Paul K. et al., 2017 [73] | √ | √ | √ | |||||||
Witrisal Klaus et al., 2016 [20] | √ | √ | √ | √ | √ | |||||
Bharadwaj R., et al., 2017 [25] | √ | √ | ||||||||
Yu J., et al., 2019 [23] | √ | √ | ||||||||
Li S., et al., 2019 [14] | √ | √ | ||||||||
Luo J., et al., 2019 [16] | √ | √ | √ | |||||||
Zourmand A., et al., 2019 [13] | √ | √ | ||||||||
Sadowski S., Spachos P., 2019 [11] | √ | √ | ||||||||
Zhang W., et al., 2019 [15] | √ | √ | ||||||||
Chen J., et al., 2019 [12] | √ | √ | ||||||||
Hoang, M., et al., 2019 [17] | √ | √ | ||||||||
Kianfar A. et al., 2020, [24] | √ | √ | ||||||||
Pérez-Solano J., et al., 2020 [68] | √ | √ | ||||||||
Grasso P., Innocente M., 2020 [74] | √ | √ | ||||||||
Simedroni R. et al., 2020 [75] | √ | √ | ||||||||
Guo S., et al., 2020 [22] | √ | √ | √ | |||||||
Bing W., et al., 2018 [56] | √ | √ | ||||||||
Schroeer G., 2018 [53] | √ | √ | ||||||||
Martínez del Horno J.., et al., 2021 [76] | √ | √ | √ | |||||||
Truong Q., et al., 2021 [77] | √ | √ | ||||||||
Vleugels R., et al., 2021 [67] | √ | √ | √ | |||||||
Hernánde, N., et al., 2021 [42] | √ | √ | ||||||||
Chen, W., 2021 [78] | √ | √ | ||||||||
Woods J., et al., 2024 [26] | √ | √ | ||||||||
Shamsollahi D., et al., 2024 [9] | √ | √ | ||||||||
Thota R., 2024 [10] | √ | √ | ||||||||
Yuxuan Z., Manyi. W., 2022 [60] | √ | √ | ||||||||
Kim J., et al., 2024 [62] | √ | √ | ||||||||
Kim D. and Jae-Young Pyun J., 2024 [61] | √ | √ | ||||||||
Ambrose A., et al., 2022 [55] | √ | √ | ||||||||
Krummenauer A., et al., 2023 [47] | √ | √ | ||||||||
Al-Khaddour M., et al., 2023 [58] | √ | √ | ||||||||
Bendavid Y., et al., 2024 [27] | √ | √ | ||||||||
Landaberea A., et al., 2022 [57] | √ | √ | ||||||||
Landaberea A., et al., 2024 [8] | √ | √ | ||||||||
Sinko S., et al., 2022 [79] | √ | √ | ||||||||
Plangger J., et al., 2023 [59] | √ | √ | ||||||||
Rana L. and Park J., 2024 [45] | √ | √ | ||||||||
Horn B., 2024 [46] | √ | √ | ||||||||
Orfanos M., et al., 2023 [44] | √ | √ |
- 3.
- Time of arrival (ToA) or time of flight (ToF). Both methods estimate the position location of a tag by measuring the time that the signal needs to travel from the transmitter to the receiver. To use this technique, both transmitter and receiver must be synchronized.
- 4.
- Two-way ranging (TWR). The two-way ranging method determines the time of flight of the RF signal and then it calculates the distance between the nodes by multiplying the time parameter by the speed of light. Figure 3 depicts the TWR method. The tag sends a start signal to the anchor at t1. The anchor receives this beacon at a timestamp t2, and then sends an answer at t3, which is received at t4 by the tag. The timestamps t2 and t3 are then sent to a tag in a data frame [80]. Finally, the tag computes the travelling time of the signal both ways and obtains a distance estimation d, which is given by:
- 5.
- Symmetrical double-sided two-way ranging (SDS-TWR). In symmetric double-sided two-way ranging, an additional cycle of sending and receiving signals is performed [80], leading to two additional timestamps, t5 and t6:
- 6.
- Near-field electromagnetic range (NFER). The NFER method refers to any radio technology employing the near-field properties of radio waves.
- 7.
- Received signal strength indication. RSSI is a term used to measure the relative quality of a received signal to a client device [76]. However, there is a direct proportion between the RSSI and the receiver–transmitter distance. This method matches the RSSI measurement to a receiver–transmitter distance. The formula for converting the RSSI measurement to distance is described below:
- r is the RSSI measured by the device,
- t is the RSSI measurement at 1 m,
- and A, B, and C are constants.
3.2. Proposed Real-Time Locating System
Algorithm 1: System algorithm for NLOS minimization and tag position estimation |
1: rij,t1, rij,t2, NLOSij ⟵ 0 |
2: while (1) do: |
3: rij,t1 ⟵ rij,t2 |
4: read anchor’s data streams rij,t2 from USB/WiFi |
5: calculate new rij,t2, dtij and drij |
6: for i in Tags do: |
7: calculate newNLOSij for tag i based on Vmax speed |
8: numberOfLOSmeasurements = []; |
9: numberOfNLOSmeasurements = []; |
10: for j in Anchors do: |
11: if dr[i][j] > 0 and NLOS[i][j] = 0 and newNLOS[i][j] = 0 then |
12: numberOfLOSmeasurements.add(rij,t2); |
13: else |
14: numberOfNLOSmeasurements.add(rij,t2); |
15: end if |
16: end for |
17: if numberOfLOSmeasurements.size() > 3 then |
18: Tag i[x][y] ⟵ Normal_Trilateration(numberOfLOSmeasurements) |
19: else if numberOfLOSmeasurements.size() = 3 then |
20: Tag i[x][y] ⟵ Normal_Trilateration(numberOfLOSmeasurements) |
21: else if (numberOfLOSmeasurements.size() = 2 then |
22: pointA[x][y], pointB[x][y] ⟵ Find_2points_with_2_Anchors(Equation (9)) |
23: if Equation (10) is satisfied then |
24: Tag i[x][y] ⟵ closest of pointA and pointB |
25: end if |
26: else if numberOfLOSmeasurements.size() + numberOfNLOSmeasurements.size() >= 3 then |
27: Tag i[x][y] ⟵ Normal_Trilateration(LOS + NLOS) |
28: else |
29: Error;//can’t calculate position for tag i |
30: end if |
31: if new position for tag i was calculated then |
32: NLOSij = newNLOSij |
33: if distance from anchor j verifies the new position, then |
34: NLOS[i][j] ⟵ 0; |
35: end if |
36: end for |
37: plotTagsToMap(); |
38: end while |
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AoA | Angle of arrival |
BLE | Bluetooth |
FTM | Fine-time measurement |
GA | Genetic algorithm |
GNSS | Global navigation satellite system |
IMU | Inertial measurement unit |
IoT | Internet of Things |
IPS | Indoor positioning system |
LIDAR | Laser imaging detection and ranging |
LOS | Line-of-sight |
NFER | Near-field electromagnetic range |
NLOS | Non-line-of-sight |
RFID | Radio frequency identification |
RMSE | Root mean square error |
RSSI | Received signal strength identification |
RTLS | Real-time locating system |
RTT | Round-trip time |
SDS-TWR | Symmetrical double-sided two-way ranging |
TDoA | Time difference of arrival |
ToA | Time of arrival |
ToF | Time-of-flight |
TWR | Two-way ranging |
UAV | Unmanned aerial vehicle |
UWB | Ultra-wideband |
VLC | Visible light communication |
VNV | Variable noise variance Kalman filter |
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RTLS Technologies | Accuracy | LOS/NLOS Impact | Range | Complexity | Cost |
---|---|---|---|---|---|
RFID | Mid | - | Low | Low | Mid |
Ultrasound | High | - | Low | Low | Mid |
Visible Light | High | - | Mid | High | High |
IMU | Mid | Low | High | High | Low |
Bluetooth | Low | High | Mid | Low | Low |
Image Recognition | High | - | Mid | High | High |
Wi-Fi 2.4 GHz RSSI | Low | High | High | Low | Low |
Wi-Fi 5 GHz RSSI | Low | High | High | Low | Low |
Wi-Fi FTM RTT | Mid | Mid | High | Low | Low |
Ultra-Wide Band | Mid | Low | High | Low | Mid |
Point (X,Y) | Normal Trilateration Algorithm Error | Proposed RLTS System Error | ||||
---|---|---|---|---|---|---|
X RMSE | Y RMSE | Euclidean RMSE | X RMSE | Y RMSE | Euclidean RMSE | |
A (4.40,3.00) (100 samples) | 0.071 | 0.068 | 0.098 | 0.060 | 0.074 | 0.095 |
B (2.50,3.20) (100 samples) | 0.143 | 0.141 | 0.201 | 0.097 | 0.110 | 0.147 |
C (2.40,3.80) (100 samples) | 0.147 | 0.136 | 0.201 | 0.118 | 0.126 | 0.173 |
D (1.25,3.90) (100 samples) | 0.131 | 0.158 | 0.205 | 0.085 | 0.079 | 0.115 |
E (1.00,3.00) (100 samples) | 0.085 | 0.112 | 0.140 | 0.070 | 0.066 | 0.096 |
F (1.60,2.00) (100 samples) | 0.060 | 0.064 | 0.088 | 0.068 | 0.069 | 0.097 |
Overall error (600 samples) | 0.112 | 0.118 | 0.163 | 0.086 | 0.090 | 0.124 |
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Sidiropoulos, A.; Bechtsis, D.; Vlachos, D. Implementing an Industry 4.0 UWB-Based Real-Time Locating System for Optimized Tracking. Appl. Sci. 2025, 15, 2689. https://doi.org/10.3390/app15052689
Sidiropoulos A, Bechtsis D, Vlachos D. Implementing an Industry 4.0 UWB-Based Real-Time Locating System for Optimized Tracking. Applied Sciences. 2025; 15(5):2689. https://doi.org/10.3390/app15052689
Chicago/Turabian StyleSidiropoulos, Athanasios, Dimitrios Bechtsis, and Dimitrios Vlachos. 2025. "Implementing an Industry 4.0 UWB-Based Real-Time Locating System for Optimized Tracking" Applied Sciences 15, no. 5: 2689. https://doi.org/10.3390/app15052689
APA StyleSidiropoulos, A., Bechtsis, D., & Vlachos, D. (2025). Implementing an Industry 4.0 UWB-Based Real-Time Locating System for Optimized Tracking. Applied Sciences, 15(5), 2689. https://doi.org/10.3390/app15052689