{"title":"Time-difference-of-arrival and frequency-difference-of-arrival estimation for signals with partially known waveform","authors":"Yan Liu, Yi Zhu, Yuan Zhang, Fucheng Guo","doi":"10.1049/sil2.12192","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"17 2","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2.12192","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/sil2.12192","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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.
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