Overlapping Laser Micro-Doppler Feature Extraction and Separation of Weak Vibration Targets

Hu, Yihua; Guo, Liren; Dong, Xiao; Xu, Shilong

doi:10.1109/lgrs.2018.2817127

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…LHD has an advantage over laser direct detection in terms of accuracy and sensitivity in acquiring the multidimensional motion information of a target [3][4][5][6][7]. In LIDAR application scenarios, such as synthetic aperture lidar and advanced remote sensing [8][9][10][11][12][13][14], etc., surface topography undulations of a moving target or atmospheric turbulence can lead to random phase fluctuations at different points in the optical field, thus introducing random phase noise [15,16]. The random phase noise eventually leads to decoherence of the target echo [8,17].…”

Section: Introductionmentioning

confidence: 99%

Laser Heterodyne Detection Based on Photon Time–Domain Differential Detection Avoiding the Effect of Decoherence Phase Noise

Guan,

Zhang,

Jia

et al. 2023

Sensors

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Laser heterodyne detection (LHD) is a key velocimetry technique that provides better accuracy and sensitivity than direct laser detection. However, random phase noise can be introduced by the surface topography of the moving target undulation or atmospheric turbulence during transmission. The random phase noise causes the target echo to undergo decoherence, resulting in degradation of the signal-to-noise ratio (SNR). Here, we propose a novel LHD method based on photon time–domain differential detection. It can infer the heterodyne spectrum of the target echo and the local oscillator light from the time intervals of the photon arrival. The time interval statistic is a relative quantity, which can effectively avoid the effect of random phase noise in LHD. With our method, the SNR of LHD can be improved in application scenarios where the target echo is decoherent. We developed a complete solution model for acquiring the heterodyne spectrum based on photon time–domain differential detection and performed proof-of-principle experiments. The experimental results show that in the presence of random phase noise, the SNR and velocity measurement error of our method are significantly better than that of the conventional method, and the larger the phase noise is, the more the SNR and velocity measurement error of our method are improved. Moreover, along with the increase in phase noise, the SNR of our method is basically unchanged, which also indicates that our method is not affected by random phase noise. This advantage is significant for photon-level weak echoes that require long detection times to be detected.

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Section: Introductionmentioning

confidence: 99%

Laser Heterodyne Detection Based on Photon Time–Domain Differential Detection Avoiding the Effect of Decoherence Phase Noise

Guan,

Zhang,

Jia

et al. 2023

Sensors

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“…Heterodyne lidar systems can achieve high detection sensitivity and better spatial resolution, which are widely used in target detection, especially in laser ranging, 1 synthetic aperture lidar 2 and vibration measurement. 3 The adoption of local oscillator (LO) light can improve the noise reduction ability, 4 and the matching characteristics between laser returns and LO are among the key factors deciding the detection performance. The waveform matching is quite challenging in laser return detection.…”

Section: Introductionmentioning

confidence: 99%

Laser echo decoherence characteristics of rough plane in heterodyne detection with truncated GSM beam

Xiao¹,

Hu²,

Zhao³

et al. 2022

Advanced Sensor Systems and Applications XII

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Rough target can cause the wavefront distortion of laser return, which shows the decoherence phenomenon and reduces the detection performance of heterodyne lidar systems. In fact, the decoherece process includes both the laser source and the rough target. The actual laser beams are usually partially coherent, and the atmospheric turbulence aggravates the coherence of laser spots on the rough target and the backscattered laser return. The backscattered laser field of rough plane is derived based on the GSM beam and the generalized Huygens-Fresnel principle. And the beam truncation effect of actual optical transceiver is also analyzed by using the hard edge aperture function. The laser return intensity variations are obtained by considering the laser beam coherence, the rough surface height fluctuation and the atmospheric turbulence. Then decoherence effects are calculated via the complex coherence degree under typical roughness parameters and laser wavelengths. For practical target, the complex coherence degree can be approximated by the Dirac delta function, and then the system efficiency and the effective coherent solid angle can also be used for further analyses. The results show a positive correlation between the decoherence effect and the roughness. The research on the scattering characteristics of rough planes expands the scattering theory and provides a reference for the design and analysis of long-range and high-precision heterodyne lidar system.

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“…The state difference matrix restricted the association of different Kalman filters with different frequency points at the next moment. In [20], multiple scattering centres were associated with the poles of the TVAR model rather than the frequencies. The association processes of the above methods are all carried out in the time-frequency domain, in which the instantaneous frequency curves of different scattering centres usually intersect and are prone to be associated incorrectly.…”

Section: Introductionmentioning

confidence: 99%

Micro‐Doppler frequency estimation and association via the inverse‐free complex SBL TVAR method

Dai

Liu

et al. 2021

IET Radar Sonar & Navi

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A micro-Doppler frequency estimation and association algorithm of the space target based on the time-varying autoregressive (TVAR) model is proposed herein. The calculation time of the existing algorithms grows significantly with the problem size, and micro-Doppler frequencies of different scattering centres are associated incorrectly where the frequencies intersect. The echo of the space target is modelled by the TVAR model, then the TVAR coefficients and instantaneous frequencies are estimated through the fast sparse Bayes learning (SBL) algorithm, finally, the micro-Doppler frequencies of different scattering centres are associated in the frequency-frequency change rate domain. The effectiveness and rapidity of the proposed algorithm is demonstrated by simulation results.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Overlapping Laser Micro-Doppler Feature Extraction and Separation of Weak Vibration Targets

Cited by 8 publications

References 13 publications

Laser Heterodyne Detection Based on Photon Time–Domain Differential Detection Avoiding the Effect of Decoherence Phase Noise

Laser Heterodyne Detection Based on Photon Time–Domain Differential Detection Avoiding the Effect of Decoherence Phase Noise

Laser echo decoherence characteristics of rough plane in heterodyne detection with truncated GSM beam

Micro‐Doppler frequency estimation and association via the inverse‐free complex SBL TVAR method

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