Volume 30 Issue 4
Jul.  2021
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JIANG Hongzhi, ZHAO Chuang, ZHAO Yongjun, “Coherent Integration Algorithm for Frequency-Agile and PRF-Jittering Signals in Passive Localization,” Chinese Journal of Electronics, vol. 30, no. 4, pp. 781-792, 2021, doi: 10.1049/cje.2021.06.001
Citation: JIANG Hongzhi, ZHAO Chuang, ZHAO Yongjun, “Coherent Integration Algorithm for Frequency-Agile and PRF-Jittering Signals in Passive Localization,” Chinese Journal of Electronics, vol. 30, no. 4, pp. 781-792, 2021, doi: 10.1049/cje.2021.06.001

Coherent Integration Algorithm for Frequency-Agile and PRF-Jittering Signals in Passive Localization

doi: 10.1049/cje.2021.06.001
  • Received Date: 2020-09-03
    Available Online: 2021-07-19
  • Publish Date: 2021-07-05
  • Increasing pulses Coherent processing interval (CPI) can effectively improve the location parameters estimation performance in passive localization. However, for a moving emitter transmitting pulses with Frequency agile and Pulse repetition frequency jittering (FA-PRFJ) in a CPI, there will exist random phase, uneven sampling and Range migration (RM), which deteriorates the estimation performance of location parameters. Aiming at long-time coherent localization parameters estimation for the above emitter, this paper proposes a joint Range difference (RD) and Range rate difference (RRD) estimation algorithm. Firstly, the signal model of a moving emitter transmitting FA-PRFJ signal is constructed, and the influence of the FA and PRFJ on coherent integration is analyzed. Secondly, the random phase induced by FA is eliminated by frequency symmetric autocorrelation function operation. Then, RD and RRD can be coherently estimated after RM correction via the modified scaled non-uniform fast Fourier transform. This method can be efficiently implemented by Fast Fourier transform (FFT), inverse FFT and FFT-based chirp-z transform without any searching operation. Simulation results demonstrate that the proposed method has a better antinoise capability with a much lower computational complexity compared with several representative methods.

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