A Novel Hybrid AoA and TDoA Solution for Transmitter Positioning

Ahmed, Sarah; Abbasi, Arash; Liu, Huaping

doi:10.1109/ipin51156.2021.9662606

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Based on the Transmitter Localization Method (LivE) [15], this approach is applicable when there is only one radiation source in the region, and the propagation model parameters are known. The method involves utilizing known monitoring data and propagation model parameter information for a least squares solution.…”

Section: The Transmitter Location Estimation Methods (Live)mentioning

confidence: 99%

Research on the development of electromagnetic spectrum map generation technology

Xie,

Yu,

Cao

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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The technique of electromagnetic spectrum map-ping enables the revelation of spatial distribution patterns within the electromagnetic environment. Precise visualization of the location information of electromagnetic spectrum signals contributes to enhancing spectrum utilization efficiency and system robustness. This technique finds wide applications in the fields of communication, navigation, radar, and other systems related to positioning and signal processing. It serves as a crucial means for modeling and simulating complex electromagnetic environments, playing a vital role in ensuring the accuracy and reliability of electromagnetic environment modeling and simulation. In this paper, we first introduce the technical background and knowledge related to electromagnetic spectrum mapping, analyzing the principles and current research status of electromagnetic spectrum mapping technology both domestically and internationally. Subsequently, we delve into a comprehensive analysis of high-precision electromagnetic spectrum mapping techniques based on sparse sampling, emphasizing potential solutions. Finally, we summarize the challenges and future directions faced by current electromagnetic spectrum mapping techniques, providing insights into their prospective trends and applications.

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Section: The Transmitter Location Estimation Methods (Live)mentioning

confidence: 99%

Research on the development of electromagnetic spectrum map generation technology

Xie,

Yu,

Cao

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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“…The forward mode will slow down the search speed. Therefore, this paper takes AAFSA4 as the basis and changes the AF movement mode of the algorithm into a jumping mechanism, that is, when the AF searches for a better position X j by the AAFSA4 method, the AF moves directly to this position, as shown in Equation (11). On the other hand, the three fields of vision can be searched with again to judge whether the forward condition is met.…”

Section: Hybrid Adaptive Vision Of Preymentioning

confidence: 99%

Target Positioning and Tracking in WSNs Based on AFSA

Lee¹,

Cheng

Lin

et al. 2023

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In wireless sensor networks (WSNs), the target positioning and tracking are very important topics. There are many different methods used in target positioning and tracking, for example, angle of arrival (AOA), time of arrival (TOA), time difference of arrival (TDOA), and received signal strength (RSS). This paper uses an artificial fish swarm algorithm (AFSA) and the received signal strength indicator (RSSI) channel model for indoor target positioning and tracking. The performance of eight different method combinations of fixed or adaptive steps, the region segmentation method (RSM), Hybrid Adaptive Vision of Prey (HAVP) method, and a Dynamic AF Selection (DAFS) method proposed in this paper for target positioning and tracking is investigated when the number of artificial fish is 100, 72, 52, 24, and 12. The simulation results show that using the proposed HAVP total average positioning error is reduced by 96.1%, and the positioning time is shortened by 26.4% for the target position. Adopting HAVP, RSM, and DAFS in target tracking, the positioning time can be greatly shortened by 42.47% without degrading the tracking success rate.

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