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      Nyquist Zone Index and Chirp Rate Estimation of LFM Signal Intercepted by Nyquist Folding Receiver Based on Random Sample Consensus and Fractional Fourier Transform

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          Abstract

          The Nyquist folding receiver (NYFR) can achieve a high-probability interception of an ultra-wideband (UWB) signal with fewer devices, while the output of the NYFR is converted into a hybrid modulated signal of the local oscillator (LO) and the received signal, which requires the matching parameter estimation methods. The linear frequency modulation (LFM) signal is a typical low probability of intercept (LPI) radar signal. In this paper, an estimation method of both the Nyquist Zone (NZ) index and the chirp rate for the LFM signal intercepted by NYFR was proposed. First, according to the time-frequency characteristics of the LFM signal, the accurate NZ and the rough chirp rate was estimated based on least squares (LS) and random sample consensus (RANSAC). Then, the information of the LO was removed from the hybrid modulated signal by the known NZ, and the precise chirp rate was obtained by using the fractional Fourier transform (FrFT). Moreover, a fast search method of FrFT optimal order was presented, which could obviously reduce the computational complexity. The simulation demonstrated that the proposed method could precisely estimate the parameters of the hybrid modulated output signal of the NYFR.

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          Analog-to-digital converter survey and analysis

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            Digital computation of the fractional Fourier transform

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              Photonic analog-to-digital converters.

              This paper reviews over 30 years of work on photonic analog-to-digital converters. The review is limited to systems in which the input is a radio-frequency (RF) signal in the electronic domain and the output is a digital version of that signal also in the electronic domain, and thus the review excludes photonic systems directed towards digitizing images or optical communication signals. The state of the art in electronic ADCs, basic properties of ADCs and properties of analog optical links, which are found in many photonic ADCs, are reviewed as background information for understanding photonic ADCs. Then four classes of photonic ADCs are reviewed: 1) photonic assisted ADC in which a photonic device is added to an electronic ADC to improve performance, 2) photonic sampling and electronic quantizing ADC, 3) electronic sampling and photonic quantizing ADC, and 4) photonic sampling and quantizing ADC. It is noted, however, that all 4 classes of "photonic ADC" require some electronic sampling and quantization. After reviewing all known photonic ADCs in the four classes, the review concludes with a discussion of the potential for photonic ADCs in the future.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                26 March 2019
                March 2019
                : 19
                : 6
                : 1477
                Affiliations
                [1 ]National Key Laboratory of Science and Technology on ATR, National University of Defense Technology, Changsha 410073, China; liuxinqun@ 123456foxmail.com (X.L.); Xiaolei_zeno@ 123456126.com (X.F.); atrchen@ 123456sina.com (Z.C.)
                [2 ]Artificial Intelligence Research Center (AIRC), National Innovation Institute of Defense Technology (NIIDT), NO.2 Fengtai South Road, Fengtai District, Beijing 100071, China
                Author notes
                [* ]Correspondence: litao_nudt@ 123456163.com ; Tel.: +86-731-8457-3464
                Author information
                https://orcid.org/0000-0002-0004-4199
                Article
                sensors-19-01477
                10.3390/s19061477
                6471702
                30917601
                e2b538f3-863e-40f3-baa0-de8d70d3db14
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 10 January 2019
                : 22 March 2019
                Categories
                Article

                Biomedical engineering
                nyquist folding receiver (nyfr),linear frequency modulated (lfm) signals,parameter estimation,random sample consensus (ransac),fractional fourier transform (frft)

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