Enhanced cooperative group localization with identification of LOS/NLOS BSs in 5G dense networks

Gazzah, Leïla; Najjar, Leila

doi:10.1016/j.adhoc.2019.03.004

Cited by 11 publications

(9 citation statements)

References 9 publications

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“…This procedure reduces the amount of transmitted raw data and alleviates the computation on the central node. The computational complexity metrics are predominantly performed for decentralized architectures in order to demonstrate their superiority in terms of computational optimization [ 77 , 90 , 109 , 131 , 132 , 137 ], whereas other performance metrics, such as the accuracy, are relegated to a second plane, as they depend primarily on technology, technique, and method used. In other words, decentralized systems offer the same positioning accuracy with fewer computational problems.…”

Section: Discussionmentioning

confidence: 99%

“…Belief propagation is mainly used because of its potential to achieve high accuracy in collaborative position estimation, but at the expense of high computational complexity. Correspondingly, the Collaborative Indoor Positioning Systems (CIPSs) that use Belief propagation tend to balance the trade-offs between the computational complexity and the positioning accuracy [ 46 , 48 , 90 , 99 , 100 , 109 , 132 , 137 ]. Trilateration and Geometric algorithms have been mainly used in those CIPSs that attempt to improve the energy consumption [ 42 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

“…As with the non-collaborative part, the most used collaborative methods present advantages and drawbacks. The six most used methods were Particle Filter [ 83 , 89 , 93 , 98 , 102 , 117 , 118 , 120 , 123 , 126 , 129 , 136 , 146 , 147 , 149 , 149 ]; Belief Propagation [ 46 , 47 , 96 , 109 , 130 , 131 , 132 , 135 ]; EKF [ 106 , 107 , 115 , 125 , 140 , 141 , 150 ]; Geometric Algorithm [ 43 , 124 , 127 , 134 , 142 , 143 ]; LS [ 45 , 49 , 133 , 137 , 148 ]; Trilateration [ 40 , 41 , 42 , 94 ]. One of the main advantages of the methods based on Particle Filter is their capability of handling non-Gaussian and non-linear estimations; however, their computational complexity increases (increment of the number of particles) as the position accuracy increases.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

101

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Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

101

View full text Add to dashboard Cite

show abstract

“…But when there is not enough LOS BSs to provide a high-accuracy initial position, this method cannot provide reliable and accurate BS selection. In channel characteristics-based BS selection method, the receiver receives and measures the channel parameters of reference signal, then compares them with the prior statistical characteristics of channel parameters in different conditions to estimate whether the channel is under LOS or NLOS conditions [ 19 ]. The variances of signal arrival angle, delay and strength are quite different in LOS and NLOS conditions.…”

Section: Introductionmentioning

confidence: 99%

Base Station Selection for Hybrid TDOA/RTT/DOA Positioning in Mixed LOS/NLOS Environment

Deng

Wang

Zheng

et al. 2020

Sensors

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The fifth generation (5G) cellular communication system is designed to support Time Difference of Arrival (TDOA), Round-Trip Time (RTT), and Direction of Arrival (DOA) measurements for indoor positioning. To mitigate the positioning error caused by non-line-of-sight (NLOS), existing base station selection methods identify channel conditions and only use line-of-sight (LOS) signals for positioning. However, different selected base station combination would lead to a different geometric dilution of precision (GDOP), base station selection based only on channel condition is not fully applicable for the hybrid positioning. This paper derives the GDOP for the hybrid TDOA, RTT, and DOA positioning, and proposes a GDOP-assisted base station selection method, which is based on both channel conditions and GDOP value changes. The simulation shows that using the proposed base station selection method could lead to higher positioning accuracy than base station selection based only on channel condition. In the simulation, in the side region of the scenario, where the change of selected base station combination causes a notable increment in GDOP value, the positioning accuracy improvement caused by the proposed method is greater than that in the center region.

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“…With the rapid development of location services, user demand is growing. Therefore, various active and passive wireless positioning technologies have attracted increasing attention and research [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Angle of Arrival Passive Location Algorithm Based on Proximal Policy Optimization

2019

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Location technology is playing an increasingly important role in urban life. Various active and passive wireless positioning technologies for mobile terminals have attracted research attention. However, positioning signals experience serious interference in high-density residential areas or in the interior of large buildings. The main type of interference is that caused by non-line-of-sight (NLOS) propagation. In this paper, we present a new method for optimizing the angle of arrival (AOA) measurement to obtain high accuracy location results based on proximal policy optimization (PPO). PPO is a new family of policy gradient methods for reinforcement learning, which can be used to adjust the sampling data under different environments using stochastic gradient ascent. Therefore, PPO can correct the NLOS propagation errors to produce a clear AOA measurement data set without building an offline fingerprinting database. Then, we used the least square method to calculate the location. The simulation result shows that the AOA passive location algorithm based on PPO produced more accurate location information.

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Enhanced cooperative group localization with identification of LOS/NLOS BSs in 5G dense networks

Cited by 11 publications

References 9 publications

Collaborative Indoor Positioning Systems: A Systematic Review

Collaborative Indoor Positioning Systems: A Systematic Review

Base Station Selection for Hybrid TDOA/RTT/DOA Positioning in Mixed LOS/NLOS Environment

Angle of Arrival Passive Location Algorithm Based on Proximal Policy Optimization

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