A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication
<p>System model of GNSS/5G three-dimensional integrated positioning.</p> "> Figure 2
<p>Two-dimensional positioning based on trilateration: (<b>a</b>) the positioning procedure when the positions of three reference nodes are accurate, and (<b>b</b>) the comparison between the positioning procedure with accurate and inaccurate reference nodes.</p> "> Figure 3
<p>The basic definitions of DBSCAN.</p> "> Figure 4
<p>Distribution of the data points before (<b>a</b>) and after (<b>b</b>) the DBSCAN process based on the TOA measurements.</p> "> Figure 5
<p>Performance comparison between the proposed algorithm and other situations for the requesting nodes.</p> "> Figure 6
<p>Performance comparison between the algorithms based on different measurements for the requesting nodes.</p> "> Figure 7
<p>Positioning results of a moving MU.</p> "> Figure 8
<p>RMSEs versus the number of particles.</p> ">
Abstract
:1. Introduction
- A GNSS/5G integrated three-dimensional positioning scheme is proposed, in which D2D communication is utilized to share the measurements and position information between the MUs. Moreover, the TOA/RSS measurements in the 5G network are combined to estimate the distances between the requesting nodes and the reference nodes.
- The three-dimensional distances between the requesting nodes and the reference nodes are considered to obtain their positions, and accordingly, a three-dimensional signal propagation model is exploited to characterize the path loss of the positioning signals in the RSS method.
- The combination algorithm of DBSCAN and PF is employed in the GNSS/5G integrated three-dimensional positioning scheme. The inaccurate position information of the cooperative nodes is considered, and the DBSCAN algorithm, based on the GNSS and TOA/RSS measurements, is developed, which reduce the position deviation of the cooperative nodes and obtain relatively accurate positions of the requesting nodes. Furthermore, a PF algorithm integrating the TOA/RSS measurements with the above results as initial values is presented.
2. Methods
2.1. System Model
2.2. Positioning Model in 5G
2.2.1. Distance Based Positioning
2.2.2. TOA-Based Distance Estimation
2.2.3. RSS-Based Distance Estimation
- Signal Propagation between the BS and the MU
- Signal Propagation between the MUs
- Distance Estimation
2.3. GNSS/5G Integrated Three-Dimensional Positioning Scheme
2.3.1. GNSS/5G Integrated Three-Dimensional Positioning Based on DBSCAN
- DBSCAN
- GNSS/5G Integrated Three-Dimensional Positioning Algorithm Based on DBSCAN
Algorithm 1: DBSCAN Algorithm Based on The TOA Measurements (DATM) |
Input: , 1: Initialization: Let be the set of core objects, be the number of clusters and be the sample data set unvisited 2: for all do 3: determine the of data point as defined in (13) 4: if then 5: is added to the set of the core objects: 6: end 7: end 8: while do 9: set the current unvisited sample data set as , select a core object and denote a queue as , then 10: while do 11: extract the first data point in 12: if then 13: and add the data points in to , 14: end 15: end 16: and the formed cluster is 17: 18: end 19: Output the clustering result and calculate the mean value of each cluster in denoted as |
2.3.2. GNSS/5G Integrated Three-Dimensional Positioning Based on PF
- PF
- GNSS/5G Integrated Three-Dimensional Positioning Algorithm Based on PF
Algorithm 2: GNSS/5G Integrated Three-Dimensional Positioning Algorithm Based on Particle Filter (GITPAPF) |
Input: the initial distribution 1: Initialization: generate the initial state of the particles according to 2: for all do 3: let be the importance distribution 4: sample the particles at time slot according to 5: calculate the weights of the particles at time slot : 6: normalize the weights: 7: resampling the particles and the weight of each of them is 8: calculate the estimated state at time slot as and output the result 9: end |
2.3.3. GNSS/5G Integrated Three-Dimensional Positioning Algorithm
Algorithm 3: GNSS/5G Integrated Three-Dimensional Positioning Algorithm (GITPA) |
Input: 1: generate the data points for DATM based on and the TOA/RSS measurements 2: conduct DATM according to the TOA and RSS measurements respectively, and each pair of the closest clusters based on both of the measurements are given the same number 3: for all do 4: calculate , , , , and according to (15), (16) and (17), respectively 5: obtain according to (18), (19) and (20) 6: end 7: let 8: project into and conduct GITPAPF for each MU in |
3. Results
- Without DBSCAN: As we know, the position information of the cooperative nodes is inaccurate. Specifically, here we use the inaccurate position information of the cooperative nodes with the GNSS and TOA/RSS measurements to calculate the positions of the requesting nodes by GITPAPF. In other words, we want to see how the inaccurate position information of the cooperative nodes influence the position results of the requesting nodes.
- Without PF: Here, we only keep the clustering process based on DATM with the joint TOA/RSS method and output the mean value of the clusters as the final results for the positions of the requesting nodes. That is, we omit the PF process in this algorithm, and the performance of PF in the proposed algorithm can be demonstrated.
- GNSS: The GNSS measurements are directly calculated to obtain the positions of the requesting nodes without any integration with the measurements from the 5G network.
- With TOA only: We only use TOA measurements to estimate the distances between the requesting nodes and the reference nodes, including the BSs and the cooperative nodes, which are further exploited in DBSCAN and PF processes in our proposed algorithm.
- With RSS only: Similarly, the RSS measurements are solely exploited in the proposed algorithm.
- GNSS: As mentioned above, the GNSS positioning results are directly added for comparison to illustrate the effect of TOA and RSS measurements more comprehensively.
4. Discussion
- It is clear in Figure 4 that, after the clustering, the data points that represent the positions of the MUs with high density gather in clusters, while the abnormal data points are deleted. In this case, the cooperative nodes can obtain more accurate position information, as well as the requesting nodes.
- We can see in Figure 5 that, in the circumstance of inaccurate cooperative nodes’ positions, we can hardly achieve accurate positioning for the requesting nodes with the PF algorithm only. Nevertheless, the positioning accuracy can be further improved by the PF algorithm after the DBSCAN process.
- The joint TOA/RSS method was used in our scheme since these two kinds of measurements can complement each other with different advantages. Figure 6 verifies that the joint TOA/RSS method can achieve the best performance compared with the algorithms with only one kind of measurement.
- Figure 7 illustrates the positioning performance of the proposed algorithm when the requesting node is on the move, and it proves that the algorithm also suits the dynamic condition well.
- In terms of PF, it is found in Figure 8 that more particles result in lower RMSEs for positioning, which is consistent with the property of the PF algorithm.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Symbol | Description |
---|---|
Set of BSs | |
Set of MUs | |
requesting nodes | |
cooperative nodes | |
The position of node | |
The distance between node and | |
The estimated distance between node and | |
The departure and arrival time of the signal from node to , respectively | |
The traveling time of the signal from node to | |
Speed of light | |
The path loss of the signal between the BS and the MU and MUs, respectively | |
The carrier frequency of the transmitted signal | |
Shadow fading of the signal between the BS and the MU | |
Flat fading coefficient of the signal between the BS and the MU and MUs, respectively | |
The transmitted and received signal from node to , respectively | |
The size of the neighborhood and the minimum data points in a cluster, respectively | |
Data set of points in DBSCAN | |
The set of reference nodes of node | |
The set of positions calculated for node | |
The posterior probability density at time slot | |
The th particle at time slot | |
The weight of the th particle at time slot | |
The number of the particles |
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Zhang, W.; Yang, Y.; Zeng, A.; Xu, Y. A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication. Remote Sens. 2022, 14, 1517. https://doi.org/10.3390/rs14061517
Zhang W, Yang Y, Zeng A, Xu Y. A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication. Remote Sensing. 2022; 14(6):1517. https://doi.org/10.3390/rs14061517
Chicago/Turabian StyleZhang, Wei, Yuanxi Yang, Anmin Zeng, and Yangyin Xu. 2022. "A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication" Remote Sensing 14, no. 6: 1517. https://doi.org/10.3390/rs14061517