连续调频定位系统的关键设计考虑因素

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Belal Al-Qudsi, Mohammed El-Shennawy, Niko Joram, Marco Gunia, Frank Ellinger
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引用次数: 0

摘要

实时定位系统(RTLS)的分布式参考节点之间存在时钟同步不准确的问题。传统系统需要定期进行时间同步,通常需要采用双向测距(TWR)时钟同步协议来消除测量误差。特别是频率调制连续波(FMCW)时基定位系统对双向测距的设计提出了独特的考虑,对其测量质量有重大影响。本文针对基于 FMCW 时基的双向无线电同步协议,提出了一个有效的操作设计图。所提出的图表示两条边界曲线的交叉区域,这两条曲线表示在给定频率带宽、频谱长度和时钟同步模糊性条件下系统的功能。该图直观地说明了测量的预期精度,在设计条件宽松时,交叉区域较大,反之亦然。此外,在进行实际系统开发之前,可以很容易地检测到工作条件的缺失。为了证明所提图表的可行性,我们在一个基本 TWR 系统的蒙特卡洛模型中评估了四种具有不同设计约束条件的情况。此外,实验测量设置也证明了所提图表的有效性。模拟和实验结果都表明,所显示的有效条件和测量精度分布非常吻合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Critical Design Considerations on Continuous Frequency Modulation Localization Systems

Critical Design Considerations on Continuous Frequency Modulation Localization Systems

Real-time locating systems (RTLSs) suffer from clock synchronization inaccuracy among their distributed reference nodes. Conventional systems require periodic time synchronization and typically necessitate a two-way ranging (TWR) clock synchronization protocol to eliminate their measurement errors. Particularly, frequency-modulated continuous-wave (FMCW) time-based location systems pose unique design considerations on the TWR that have a significant impact on the quality of their measurements. In this paper, a valid operation design diagram is proposed for the case of an FMCW time-based TWR synchronization protocol. The proposed diagram represents an intersection area of two boundary curves that indicate the functionality of the system at a given frequency bandwidth, spectral length, and clock synchronization ambiguity. It presents an intuitive illustration of the measurement’s expected accuracy by indicating a larger intersection area for relaxed design conditions and vice versa. Furthermore, the absence of a working condition can easily be detected before proceeding with the actual system development. To demonstrate the feasibility of the proposed diagram, four scenarios with different design constraints were evaluated in a Monte-Carlo model of a basic TWR system. Moreover, an experimental measurement setup demonstrated the validity of the proposed diagram. Both the simulation and experimental outcomes show that the indicated valid conditions and the distribution of the measurements’ accuracy are in very good agreement.

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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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