Time-difference-of-arrival and frequency-difference-of-arrival estimation for signals with partially known waveform

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC IET Signal Processing Pub Date : 2023-02-22 DOI:10.1049/sil2.12192
Yan Liu, Yi Zhu, Yuan Zhang, Fucheng Guo
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引用次数: 1

Abstract

In many passive localization applications, the reference waveform of the received electromagnetic signals is partly known. The received signals in such scenarios are formulated by modelling the relationship between the received data and the known reference signal waveform and the parameters of interest, such as time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA). By exploiting the prior information carried by the known waveform of the reference signal, the negative impact of random noise can be significantly reduced. Following this guideline, a coherent and an incoherent method is proposed to estimate the TDOA and FDOA parameters between two moving receivers. The Cramer-Row lower bound of the TDOA and FDOA estimation accuracy is also analysed. Simulation results show the advantage of the proposed coherent method in TDOA and FDOA estimation precision over its counterparts, which partially demonstrates that effective exploitation of the known signal waveform can largely improve the performance of TDOA and FDOA estimation.

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波形部分已知的信号的到达时间差和到达频率差估计
在许多无源定位应用中,接收到的电磁信号的参考波形是部分已知的。通过对接收数据和已知参考信号波形之间的关系以及感兴趣的参数(例如到达时间差(TDOA)和到达频率差(FDOA))进行建模,来制定这种场景中的接收信号。通过利用参考信号的已知波形所携带的先验信息,可以显著降低随机噪声的负面影响。根据这一准则,提出了一种相干和非相干方法来估计两个移动接收机之间的TDOA和FDOA参数。分析了时差和频差估计精度的Cramer Row下界。仿真结果表明,所提出的相干方法在时差和频差估计精度上优于同类方法,部分表明有效利用已知信号波形可以大大提高时差和频偏估计的性能。
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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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