AOA estimation for coexisting UWB signals with multipath channels

Liu, Hsin-Chin; Hsuan, Cheng-Wei

doi:10.1109/temu.2014.6917756

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Figure 6 presents comparisons for the CDF performance at SDR = 2 m, SDA = 1

, i.e., a typical measurement noise level in existing literature [ 28 , 29 ]. From it, we definitely see the superior performance of the proposed algorithm, i.e., even with only two LOS-ANs, the NI-LS method yields about nine meters errors with probability higher than 0.95.…”

Section: Simulation and Analysismentioning

confidence: 99%

NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks

Hua

Yin

et al. 2018

Sensors

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The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.

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“…Figure 6 presents comparisons for the CDF performance at SDR = 2 m, SDA = 1

Section: Simulation and Analysismentioning

confidence: 99%

NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks

Hua

Yin

et al. 2018

Sensors

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“…5, the value of G M (1, 2) is 2, which indicates that the RSRP changing pair (1, 2) occurs twice in the vector R c . Also, the G M (3,4) value is 1 indicating that the RSRP changing pair (3,4) only occurs once in the vector R c . And then the normalized RSRP co-occurrence matrix P M ∈ R m×m is obtained by normalizing G M with the sum frequency of all RSRP changing pairs n − 1:…”

Section: Ue Motion State Classification Algorithmmentioning

confidence: 99%

“…Furthermore, even with auxiliary information, it has to take a long time to calculate the position and the result is inaccurate, especially for the fast-moving UEs. Another option is to implement the commonly used ranging techniques, including time of arrival (TOA), angle of arrival (AOA), observed time difference of arrival (OTDOA) [2][3][4]. Some hybrid techniques are also proposed for ranging, such as TOA/AOA and TOA/TDOA [5,6].…”

Section: Introductionmentioning

confidence: 99%

RSRP difference elimination and motion state classification for fingerprint-based cellular network positioning system

Jin

Zhang

et al. 2018

Telecommun Syst

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In recent years, the widespread availability of wireless communication has stimulated research of positioning service for cellular networks. Based on the minimization of drive-test introduced in 3GPP R10, it not only can be used to collect radio measurements and associated location information for network performance assessment, but also be used to build radio map for the fingerprint-based cellular network positioning system. However, due to the UE diversity, reference signal receiving power (RSRP) difference seriously degrades the positioning performance. In addition, UE motion changes the fingerprint length, which leads to fingerprint misalignment. Therefore, in this paper we propose to use multi-dimensional scaling algorithm to eliminate UE RSRP difference. By analyzing the relative RSRP difference of each pair of UE, we can eliminate RSRP difference in both offline and online. We also use the pattern recognition theory to classify UE motion for fingerprint alignment. We separated RSRP into two groups according to the UE motion state. The two groups of fingerprints are classified separately in offline and online. We implemented the proposed method in a real urban area and evaluated its positioning performance. The experiment results indicated our method can achieve a better positioning performance in cellular network positioning system.

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An Accuracy and Real-Time Commercial Localization System in LTE Networks

Wei

2020

IEEE Access

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In this paper, an ARTLoc (accuracy and real-time commercial localization) system is proposed, which is applicable for positioning the Minimization of Drive-Tests(MDT) data in the long-termevolution(LTE) cellular communication network system. This system utilizes a collaborative filtering algorithm to ensure the robustness and accuracy of the fingerprint database when both base station position and GPS MDT data are abnormal in commercial communication service provider(CSP) MDT localization scene. Then, a fast matching KNN algorithm including coarse matching and fine matching is proposed to improve the location efficiency and reduce the location cost under the premise of ensuring high location accuracy. The results of experiments conducted in an in-service LTE network using more than 1000 LTE base station demonstrate that the proposed technique yields a location accuracy of 65.7 meters(@67%) with DT data and 62.1 meters(@67%) with massive GPS MDT data(over 10 million), which provides at least 184.2% and 145.2% enhancement in accuracy respectively compared to the traditional technology. In addition, our system provides a more than 7 times improvement in location efficiency compared to traditional technology. This proposed localization technique is applicable in network optimization and Operation and Maintenance(O&M) to assist CSP to reduce their operating expense(OPEX) by positioning those MDT data without GPS location.

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AOA estimation for coexisting UWB signals with multipath channels

Cited by 6 publications

References 17 publications

NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks

NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks

RSRP difference elimination and motion state classification for fingerprint-based cellular network positioning system

An Accuracy and Real-Time Commercial Localization System in LTE Networks

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