Long‐time coherent accumulation algorithm based on acceleration blind estimation

Wang, Zhuo; Zheng, Xuehe; Xiaolan, Chang

doi:10.1049/joe.2019.0636

Cited by 1 publication

(1 citation statement)

References 15 publications

(14 reference statements)

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“…Based on Fourier transform, the improved axis rotation discrete chirp-Fourier transform [18] and modified radon Fourier transform [19] were proposed to realize the compensations of range and Doppler migrations. In addition, a long-time coherent integration algorithm was proposed to compensate the phase of original echo signal by extracting the relative acceleration information carried in the echo signal, such that the compensated signals can be refocused in the frequency domain [20].…”

Section: Introductionmentioning

confidence: 99%

Passive Detection of Ship-Radiated Acoustic Signal Using Coherent Integration of Cross-Power Spectrum with Doppler and Time Delay Compensations

Guo

Piao

Guo

et al. 2020

Sensors

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Passive sonar is widely used for target detection, identification and classification based on the target radiated acoustic signal. Under the influence of Doppler, generated by relative motion between the moving target and the sonar array, the received ship-radiated acoustic signals are non-stationary and time-varying, which has a negative effect on target detection and other fields. In order to reduce the influence of Doppler and improve the performance of target detection, a coherent integration method based on cross-power spectrum is proposed in this paper. It can be concluded that the frequency shift and phase change in the cross-power spectrum obtained by each pair of data segments can be corrected with the compensations of time scale (Doppler) factor and time delay. Moreover, the time scale factor and time delay can be estimated from the amplitude and phase of the original cross-power spectrum, respectively. Therefore, coherent integration can be implemented with the compensated cross-power spectra. Simulation and experimental data processing results show that the proposed method can provide sufficient processing gains and effectively extract the discrete spectra for the detection of moving targets.

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Section: Introductionmentioning

confidence: 99%