Research on the detection probability curve characteristics of long-range target based on SPAD array lidar

Zhang, Xin; Sun, Jianfeng; Li, Sining; Zhang, Yinbo; Liu, Di; Zhang, Hailong

doi:10.1016/j.infrared.2022.104325

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…However, the signal intensity is influenced heavily by the target characteristics, which in most cases results in the neglect of the low-reflection objects. Current study results have shown that the target characteristics are able to be extracted from the signal waveform [6][7] . Accurate target characterization relies on photon accumulation for a long time.…”

Section: Introductionmentioning

confidence: 91%

Confidence interval guided single photon LiDAR depth imaging

Chang,

Li,

et al. 2023

Optoelectronic Imaging and Multimedia Technology X

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Section: Introductionmentioning

confidence: 91%

Confidence interval guided single photon LiDAR depth imaging

Chang,

Li,

et al. 2023

Optoelectronic Imaging and Multimedia Technology X

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“…For the Gm-APD array LiDAR, the following device imaging analysis we performed. The Gm-APD array LiDAR uses the photon time-of-flight method to collect depth information of the target [25], and uses the time-to-digital converter (TDC) for timing [26], [27]. Each pixel in the Gm-APD array is given the time-of-flight recorded by the TDC at the corresponding TDC trigger frequency.…”

Section: B Motivations and Contributionsmentioning

confidence: 99%

Gm-APD LiDAR Single-Source Data Self-Guided: Obtaining High-Resolution Depth Map

Song

Sun

Yan

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Geiger-mode avalanche photodiode (Gm-APD) array LiDAR has become the current research focus due to its sensitive response, high precision, and easy integration. However, due to the limitations of the fabrication process and manufacturing cost, the images collected by Gm-APD array LiDAR have very serious image low-resolution problems. Since the intensity map and depth map of the target can be obtained simultaneously by relying on single-source data from Gm-APD array LiDAR, we propose a Gm-APD LiDAR single-source data self-guided method. We propose to first perform Gm-APD LiDAR intensity map superresolution, and then use the processed intensity map with the corresponding depth map for guided superresolution. The advantage of this process is that instead of using high-resolution (HR) imaging devices from different domains, it relies only on single-source data from Gm-APD LiDAR to obtain HR depth map, thus eliminating the need for additional image registration work and providing wider applicability. We investigate the feasibility of the proposed singlesource data processing method and evaluate our method on the real Gm-APD LiDAR single-source data with an average peak signal-to-noise ratio of 42.21, which better preserves the original distance information of the targets while providing visually sharper outputs.

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“…With photon-level detection sensitivity and picosecond time resolution, such LIDARs are promising tools for the precise detection of remote and weak signals 1 – 5 However, during object detection and imaging with the GM-APD LIDAR, carriers may be generated in the presence of background noise, target echo, and internal dark noise, leading to an avalanche of current output 6 – 8 Limited by the first photon detection system of the GM-APD, a single detection corresponds to only one carrier, and thus, the signal and noise cannot be distinguished.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4][5] However, during object detection and imaging with the GM-APD LIDAR, carriers may be generated in the presence of background noise, target echo, and internal dark noise, leading to an avalanche of current output. [6][7][8] Limited by the first photon detection system of the GM-APD, a single detection corresponds to only one carrier, and thus, the signal and noise cannot be distinguished. Therefore, the multi-frame data detected by the GM-APD LIDAR must be processed to obtain depth information of the target.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fading Kalman depth estimation with chi-square hypothesis testing for Geiger-mode avalanche photo diode LIDAR

et al. 2023

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.The operation of a Geiger-mode avalanche photo diode (GM-APD) LIDAR is severely disturbed by background noise, which makes it challenging to rapidly and accurately estimate the target depth. Therefore, we propose an adaptive fading Kalman depth estimation technique based on chi-square hypothesis testing for GM-APD LIDARs. First, by analyzing the consistency of the echo photon distribution between adjacent pixels of the GM-APD, the pixels are fused to rapidly obtain the echo data of the target surface, thereby accelerating the depth estimation process. Second, we design a chi-square hypothesis test condition based on the statistical characteristics of the innovation vector, which can help evaluate whether the fading factor is introduced at the current moment, promote the convergence of the algorithm, and reduce the depth estimation time. Third, we propose a fading memory index weighted method to adaptively adjust the weight of the observed values to accurately estimate the innovation matrix covariance and determine the optimal fading factor. We demonstrate the effectiveness of the proposed algorithm through simulations and experiments. The results show that the proposed algorithm can rapidly and accurately estimate the target depth in the presence of strong background noise.

show abstract

Research on the detection probability curve characteristics of long-range target based on SPAD array lidar

Cited by 6 publications

References 18 publications

Confidence interval guided single photon LiDAR depth imaging

Confidence interval guided single photon LiDAR depth imaging

Gm-APD LiDAR Single-Source Data Self-Guided: Obtaining High-Resolution Depth Map

Adaptive fading Kalman depth estimation with chi-square hypothesis testing for Geiger-mode avalanche photo diode LIDAR

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