Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Non-line-of-sight snapshots and background mapping with an active corner camera.

Nature communications·2023
Same author

SPADs and SiPMs Arrays for Long-Range High-Speed Light Detection and Ranging (LiDAR).

Sensors (Basel, Switzerland)·2021
Same author

Spot Tracking and TDC Sharing in SPAD Arrays for TOF LiDAR.

Sensors (Basel, Switzerland)·2021
Same author

Monitoring the motor cortex hemodynamic response function in freely moving walking subjects: a time-domain fNIRS pilot study.

Neurophotonics·2021
Same author

Biometric Signals Estimation Using Single Photon Camera and Deep Learning.

Sensors (Basel, Switzerland)·2020
Same author

0.16 µm⁻BCD Silicon Photomultipliers with Sharp Timing Response and Reduced Correlated Noise.

Sensors (Basel, Switzerland)·2018
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: May 25, 2026

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
07:38

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

10.2K

Statistical Modelling of SPADs for Time-of-Flight LiDAR.

Alfonso Incoronato1, Mauro Locatelli1, Franco Zappa1

  • 1Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano, Italy.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

This study presents a statistical model for Time-of-Flight (TOF) Light Detection and Ranging (LiDAR) systems. The model accurately predicts TOF histogram distributions, accounting for detector and timing non-idealities, even under high background light.

Keywords:
Light Detection and Ranging (Lidar)Monte Carlo simulationsSilicon Photo-Multipliers (SiPM)Single Photon Avalanche Diode (SPAD)Time-of-Flight (TOF) measurements

More Related Videos

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

15.1K
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.8K

Related Experiment Videos

Last Updated: May 25, 2026

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
07:38

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

10.2K
A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

15.1K
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.8K

Area of Science:

  • Photonics and Optical Engineering
  • Sensor Technology
  • Computational Modeling

Background:

  • Time-of-Flight (TOF) Light Detection and Ranging (LiDAR) is crucial for distance measurement and 3D mapping.
  • Single Photon Avalanche Diodes (SPADs) with integrated Time-to-Digital Converters (TDCs) enable precise single-photon TOF measurements.
  • TOF histogram accumulation is essential for distinguishing laser returns from ambient light and calculating distances.

Purpose of the Study:

  • To develop a detailed statistical model of the LiDAR acquisition chain.
  • To predict TOF histogram distributions under various operating conditions, including high background light.
  • To account for non-idealities in SPAD detectors and timing electronics for accurate system design and data analysis.

Main Methods:

  • Statistical modeling of the entire LiDAR acquisition chain, from SPAD to TDC and histogram processing.
  • Monte Carlo simulations to validate the model's predictions across different scenarios.
  • Inclusion of SPAD non-idealities (hold-off time, afterpulsing, crosstalk) and TDC non-idealities (dead-time, limited storage, sharing).

Main Results:

  • The statistical model shows perfect matching with Monte Carlo simulations.
  • The model accurately predicts TOF histogram distortions caused by high background light and pile-up effects.
  • Demonstrated the ability to reverse-engineer and extract original LiDAR signals from distorted TOF data.

Conclusions:

  • The developed statistical model provides a robust tool for predicting LiDAR performance and designing system components.
  • The model effectively handles SPAD and TDC non-idealities and high ambient light conditions.
  • This work enables accurate distance computation and signal recovery in challenging LiDAR operating environments.