Suppressing the influence of GM-APD coherent lidar saturation by signal modulation

Shi, Xiang; Lu, Wei; Sun, Jianfeng; Ge, Weijie; Zhang, Hailong; Li, Sining

doi:10.1016/j.ijleo.2023.170619

Cited by 3 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Geiger-mode avalanche photodiode (GM-APD) light detection and ranging (lidar) is a laser active imaging radar that uses GM-APD photon-level detection sensitivity to achieve long-range target weak echo signal detection, which has the advantages of high detection sensitivity, long-acting distance, and high distance resolution [1,2] and has significant applications in target detection, remote sensing, military guidance, security monitoring, and other fields. However, GM-APD lidar is seriously disturbed by noise, such as daylight and atmospheric backscattering, and when the signal-to-background ratio (SBR, the ratio of target echo signal photons to background noise photons in the gate) of the laser echo signal is low, the target echo signal is easily drowned in the noise, resulting in the recovered target depth image containing a large amount of noise.…”

Section: Introductionmentioning

confidence: 99%

A Fractional-Order Total Variation Regularization-Based Method for Recovering Geiger-Mode Avalanche Photodiode Light Detection and Ranging Depth Images

Xie

Wang

et al. 2023

Fractal Fract

View full text Add to dashboard Cite

High-quality image restoration is typically challenging due to low signal–to–background ratios (SBRs) and limited statistics frames. To address these challenges, this paper devised a method based on fractional-order total variation (FOTV) regularization for recovering Geiger-mode avalanche photodiode (GM-APD) light detection and ranging (lidar) depth images. First, the spatial differential peak-picking method was used to extract the target depth image from low SBR and limited frames. FOTV regularization was introduced based on the total variation regularization recovery model, which incorporates the fractional-order differential operator, in order to realize FOTV-regularization-based depth image recovery. These frameworks were used to establish an algorithm for GM-APD depth image recovery based on FOTV. The simulation and experimental results demonstrate that the devised FOTV-recovery algorithm improved the target reduction degree, peak signal–to–noise ratio, and structural similarity index measurement by 76.6%, 3.5%, and 6.9% more than the TV, respectively, in the same SBR and statistic frame conditions. Thus, the devised approach is able to effectively recover GM-APD lidar depth images in low SBR and limited statistic frame conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

A Fractional-Order Total Variation Regularization-Based Method for Recovering Geiger-Mode Avalanche Photodiode Light Detection and Ranging Depth Images

Xie

Wang

et al. 2023

Fractal Fract

View full text Add to dashboard Cite

show abstract

“…Lidar has been widely used in terrain mapping, forestry exploration, autonomous driving, military defense, and other fields due to its high resolution, strong anti-interference ability, and fast response speed [1][2][3][4]. Geiger-mode avalanche photodiode (GM-APD) laser radar can detect single-photon echo signals and remote weak signals [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Fractional-Order Total Variation Geiger-Mode Avalanche Photodiode Lidar Range-Image Denoising Algorithm Based on Spatial Kernel Function and Range Kernel Function

Wei,

Wang,

Xie

et al. 2023

Fractal Fract

View full text Add to dashboard Cite

A Geiger-mode avalanche photodiode (GM-APD) laser radar range image has much noise when the signal-to-background ratios (SBRs) are low, making it difficult to recover the real target scene. In this paper, based on the GM-APD lidar denoising model of fractional-order total variation (FOTV), the spatial relationship and similarity relationship between pixels are obtained by using a spatial kernel function and range kernel function to optimize the fractional differential operator, and a new FOTV GM-APD lidar range-image denoising algorithm is designed. The lost information and range anomalous noise are suppressed while the target details and contour information are preserved. The Monte Carlo simulation and experimental results show that, under the same SBRs and statistical frame number, the proposed algorithm improves the target restoration degree by at least 5.11% and the peak signal-to-noise ratio (PSNR) by at least 24.6%. The proposed approach can accomplish the denoising of GM-APD lidar range images when SBRs are low.

show abstract

Fractional-Order Super-Resolution Reconstruction Algorithm for GM-APD Lidar Distance Images Based on Convex Set Projection

Li,

Wang,

Liu

et al. 2024

IFAC-PapersOnLine

View full text Add to dashboard Cite

Suppressing the influence of GM-APD coherent lidar saturation by signal modulation

Cited by 3 publications

References 17 publications

A Fractional-Order Total Variation Regularization-Based Method for Recovering Geiger-Mode Avalanche Photodiode Light Detection and Ranging Depth Images

A Fractional-Order Total Variation Regularization-Based Method for Recovering Geiger-Mode Avalanche Photodiode Light Detection and Ranging Depth Images

Fractional-Order Total Variation Geiger-Mode Avalanche Photodiode Lidar Range-Image Denoising Algorithm Based on Spatial Kernel Function and Range Kernel Function

Fractional-Order Super-Resolution Reconstruction Algorithm for GM-APD Lidar Distance Images Based on Convex Set Projection

Contact Info

Product

Resources

About