期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2016
卷号：14
期号：1
页码：91-100
DOI：10.12928/telkomnika.v14i1.2345
语种：English
出版社：Universitas Ahmad Dahlan
摘要：Abstract Cognitive radio (CR) provides a theoretical foundation to achieve the cognitive function and collaborative function for the positioning nodes. Under this trend, the cognitive positioning system (CPS) has emerged. But the limitation of the traditional range accuracy adaptation criterion based on Cramér-Rao Lower Bound (CRLB) makes it very diffcult to put CPS into practices. To overcome this problem, it is necessary to further study the criterion in complex noise environment. Based on the time of arrival (TOA) location estimation algorithm, we analyze the performance of the range accuracy adaptation algorithm, which take the Ziv-Zakai lower bound information (ZZLB) as the CPS parameter optimization criterion. Simulation results show that the bound can provide more complete range accuracy adaptation information compared with CRLB. Furthermore, we can improve the positioning accuracy by means of enhancing the system signal-to-noise ratio (SNR), adjusting the system bandwidth and increasing the observation duration.
其他摘要：Abstract
Cognitive radio (CR) provides a theoretical foundation to achieve the cognitive function and collaborative function for the positioning nodes. Under this trend, the cognitive positioning system (CPS) has emerged. But the limitation of the traditional range accuracy adaptation criterion based on Cramér-Rao Lower Bound (CRLB) makes it very diffcult to put CPS into practices. To overcome this problem, it is necessary to further study the criterion in complex noise environment. Based on the time of arrival (TOA) location estimation algorithm, we analyze the performance of the range accuracy adaptation algorithm, which take the Ziv-Zakai lower bound information (ZZLB) as the CPS parameter optimization criterion. Simulation results show that the bound can provide more complete range accuracy adaptation information compared with CRLB. Furthermore, we can improve the positioning accuracy by means of enhancing the system signal-to-noise ratio (SNR), adjusting the system bandwidth and increasing the observation duration.
