scholarly journals Fast Joint Estimation of Time of Arrival and Angle of Arrival in Complex Multipath Environment Using OFDM

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 60613-60621 ◽  
Author(s):  
Haiyun Xu ◽  
Yankui Zhang ◽  
Bin Ba ◽  
Daming Wang ◽  
Xiangzhi Li
Keyword(s):  
Joint Estimation ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Multipath Environment ◽  
Estimation Of Time

scholarly journals Non-Line-of-Sight Error Mitigation in Wireless Communication Systems

2011 ◽  
Vol 1 ◽  
pp. 173-177
Author(s):  
Szu Lin Su ◽  
Yi Wen Su ◽  
Ho Nien Shou ◽  
Chien Sheng Chen
Keyword(s):  
Communication Systems ◽  
Hybrid Methods ◽  
Mobile Station ◽  
Base Stations ◽  
Superior Performance ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Error Statistics ◽  
Non Line Of Sight

When there is non-line-of-sight (NLOS) path between the mobile station (MS) and base stations (BSs), it is possible to integrate many kinds of measurements to achieve more accurate measurements of the MS location. This paper proposed hybrid methods that utilize time of arrival (TOA) at five BSs and angle of arrival (AOA) information at the serving BS to determine the MS location in NLOS environments. The methods mitigate the NLOS effect simply by the weighted sum of the intersections between five TOA circles and the AOA line without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods always give superior performance than Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).

Performance Evaluation of Geolocation Based Opportunistic Spectrum Access in Cloud-Assisted Cognitive Radio Networks

2016 ◽  
Vol 4 (1) ◽  
pp. 24-41
Author(s):  
Swetha Reddy ◽  
Isaac Cushman ◽  
Danda B. Rawat ◽  
Min Song
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Estimation Error ◽  
Time Of Arrival ◽  
Spectrum Access ◽  
Angle Of Arrival ◽  
Secondary Users ◽  
Probability Of False Alarm ◽  
Computing Platform ◽  
Overall Performance

The popularity of cloud-assisted database-driven cognitive radio network (CRN) has increased significantly due to three main reasons; reduced sensing uncertainties (caused by the use of spectrum scanning and sensing techniques), FCC mandated use of a database for storing and utilizing idle channels, and leveraging cloud computing platform to process big data generated by wideband sensing and analyzing. In database-driven CRN, secondary users periodically query the database to find idle channels for opportunistic communications where secondary users use their geolocation (with the help of Global Positioning System - GPS) to find idle channels for given location and time. Use of GPS makes the overall CRN vulnerable where malicious users falsify their geolocations through GPS spoofing to find more channels. The other main drawback of GPS is estimation error while finding location of users and idle bands. Due to this there will be probability of misdetection and false alarm which will have its effect on overall performance and efficiency of the system. In this paper, the authors present a three-stage mechanism for detecting GPS spoofing attacks using angle of arrival, received signal strength and time of arrival. They also evaluate the probability of misdetection and probability of false alarm in this system while detecting location of secondary users. The authors evaluate the performance of the proposed approach using numerical results.

scholarly journals Probability Weighting Localization Algorithm Based on NLOS Identification in Wireless Network

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Shixun Wu ◽  
Shengjun Zhang ◽  
Kai Xu ◽  
Darong Huang
Keyword(s):  
Base Station ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Probability Weighting ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Chi Square ◽  
Home Base ◽  
Angle Measurements ◽  
Non Line Of Sight

In this paper, a localization scenario that the home base station (BS) measures time of arrival (TOA) and angle of arrival (AOA) while the neighboring BSs only measure TOA is investigated. In order to reduce the effect of non-line of sight (NLOS) propagation, the probability weighting localization algorithm based on NLOS identification is proposed. The proposed algorithm divides these range and angle measurements into different combinations. For each combination, a statistic whose distribution is chi-square in LOS propagation is constructed, and the corresponding theoretic threshold is derived to identify each combination whether it is LOS or NLOS propagation. Further, if those combinations are decided as LOS propagation, the corresponding probabilities are derived to weigh the accepted combinations. Simulation results demonstrate that our proposed algorithm can provide better performance than conventional algorithms in different NLOS environments. In addition, computational complexity of our proposed algorithm is analyzed and compared.

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