Application of an Adaptive UKF in UWB Indoor Positioning

Fu, Jun; Fu, Yang; Xu, Da

doi:10.1109/cac48633.2019.8996692

Cited by 11 publications

(9 citation statements)

References 10 publications

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“…Utilising the benefits of both strategies, CNN and the Archimedes-Based Salp Swarm Algorithm were coupled.The CNN can conduct feature extraction and classification, and the swarm algorithm can help the CNN's performance by optimising its hyperparameters.The CIR features extracted from the channel are forwarded to the CNN structure in the proposed localization stage [23,26] Using a hybrid method, the overarching goal is to increase the reliability and effectiveness of NLOS identification. When the targeted MIMO is any location of the interested area, the position can be estimated with the weighted centroid approach and can be formulated exactly as,…”

Section: Cnn-hassa Based Localizationmentioning

confidence: 99%

Machine Learning-Based Intelligent Localization Technique for Channel Classification in Massive MIMO

Ghrabat,

Zhu,

Wang

2024

Preprint

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The widespread adoption of wireless communication has led to a rapid increase in the utilization of Multiple-Input-Multiple-Output (MIMO) technology. This advancement enables the simultaneous transmission of multiple data streams through the use of multiple transmitters and receivers. MIMO leverages the radio wave phenomenon known as multipath, where transmitted signals encounter various obstacles, arriving at the antenna at different angles and times. In 5G networks, an inherent challenge in Massive MIMO Localization arises from the non-line-of-sight (NLOS) problem. This issue significantly hampers positioning accuracy, emphasizing the need for innovative solutions. This work proposes an intelligent localization technique based on NLOS identification and mitigation. To achieve this, we first propose a Convolutional Neural network (CNN) based hybrid Archimedes based Salp Swarm Algorithm (HASSA) technique to detect the NLOS and LOS and therein estimate the location accuracy. The accuracy can be analyzed by considering the angle of arrival of signals (AOA), Threshold-based Time of arrival (TOA), Time difference of arrival (TDOA) from different antennas. Henceforth, a novel Reinforcement Learning based optimization approach is used for the mitigation of NLOS in the radio wave propagation path. We use Ensemble Deep Deterministic Policy Gradient-based approach (EDDPG) based Honey Badger algorithm (HBA) for the aforementioned process. This also reduces the computational complexity. Simulation of this approach deems different scenarios and considers different parameters and compared with different state-of-art works. From the simulation results, we observed that our proposed approach can be used for the identification and detection of the LOS and NLOS components and also precisely enhance the localization than the other approaches.

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Section: Cnn-hassa Based Localizationmentioning

confidence: 99%

Machine Learning-Based Intelligent Localization Technique for Channel Classification in Massive MIMO

Ghrabat,

Zhu,

Wang

2024

Preprint

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“…The KF family has received the favor of implementers of indoor localization algorithms because, despite its complexity, the KF relies on matrix operations that most operating systems integrate natively. Thus, the computation can be done efficiently on most systems, in near real-time, and KF techniques are widely successful for UWB-based localization [10][11][12]. However, you should be aware of several limitations:…”

Section: Kalman Filtermentioning

confidence: 99%

Ranging and Positioning with UWB

Henry¹

2023

UWB Technology - New Insights and Developments

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Indoor location is one of the key use cases enabled by UWB for navigation and asset tracking. The 802.15.4a and 802.15.4z standards describe several techniques for determining the distance between a mobile device client and a set of static anchors. Two of them (SS-TWR and DS-TWR) use a bidirectional exchange between the client and the anchor, with the resulting distance being calculated on the initiating side (SS-TWR) or both sides (DS-TWR). OWR does not require an exchange and simply relies on the comparison of arrival times of signals (TDoA). With UL-TDoA, the time of arrival of the client signal is compared on several anchors, drawing distance hyperbolae from which the client location is deduced. With DL-TDoA, the reverse happens: the client compares the time of arrival of signals from several anchors and deduces its position. A third family of techniques is not described in the Standard but is commonly implemented in the field: AoA, where a comparison of the phase of the signal among two or more antennas is used to compute the direction of the sender. From these elements, a location engine computes the mobile device’s position. This chapter examines these techniques in detail.

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“…This process requires a "no memory" property: the probability distribution of the next state can only be determined by the current state. There have been many studies based on Markov chains in recent years [8].…”

Section: Markov Algorithmmentioning

confidence: 99%

UWB System and Algorithms for Indoor Positioning

Gao

Postolache

Yang

et al. 2021

2021 Telecoms Conference (ConfTELE)

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This research work presents of study of ultra-wide band (UWB) indoor positioning considering different type of obstacles that can affect the localization accuracy. In the actual warehouse, a variety of obstacles including metal, board, worker and other obstacles will have NLOS (non-line-of-sight) impact on the positioning of the logistics package, which influence the measurement of the distance between the logistics package and the anchor , thereby affecting positioning accuracy. A new developed method attempts to improve the accuracy of UWB indoor positioning, through and improved positioning algorithm and filtering algorithm. In this project, simulate the warehouse environment in the laboratory, several simulation proves that the used Kalman filter algorithm and Markov algorithm can effectively reduce the error of NLOS. Experimental validation is carried out considering a mobile tag mounted on a robot platform.

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Application of an Adaptive UKF in UWB Indoor Positioning

Cited by 11 publications

References 10 publications

Machine Learning-Based Intelligent Localization Technique for Channel Classification in Massive MIMO

Machine Learning-Based Intelligent Localization Technique for Channel Classification in Massive MIMO

Ranging and Positioning with UWB

UWB System and Algorithms for Indoor Positioning

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