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1. First Arrival Differential LiDAR

   https://doi.org/10.1109/ICCP54855.2022.9887683  

   Zhang, Tianyi; White, Mel J.; Dave, Akshat; Ghajari, Shahaboddin; Raghuram, Ankit; Molnar, Alyosha C.; Veeraraghavan, Ashok (August 2022, IEEE International Conference on Computational Photography (ICCP).)
2. Quantum-enhanced Doppler lidar

   https://doi.org/10.1038/s41534-022-00662-9  

   Reichert, Maximilian; Di Candia, Roberto; Win, Moe Z.; Sanz, Mikel (December 2022, npj Quantum Information)

   Abstract We propose a quantum-enhanced lidar system to estimate a target’s radial velocity, which employs squeezed and frequency-entangled signal and idler beams. We compare its performance against a classical protocol using a coherent state with the same pulse duration and energy, showing that quantum resources provide a precision enhancement in the estimation of the velocity of the object. We identify three distinct parameter regimes characterized by the amount of squeezing and frequency entanglement. In two of them, a quantum advantage exceeding the standard quantum limit is achieved assuming no photon losses. Additionally, we show that an optimal measurement to attain these results in the lossless case is frequency-resolved photon counting. Finally, we consider the effect of photon losses for the high-squeezing regime, which leads to a constant factor quantum advantage higher than 3 dB in the variance of the estimator, given a roundtrip lidar-to-target-to-lidar transmissivity larger than 50%. 

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3. EMI-LiDAR: Uncovering Vulnerabilities of LiDAR Sensors in Autonomous Driving Setting using Electromagnetic Interference

   https://doi.org/10.1145/3558482.3590192  

   Bhupathiraju, Sri Hrushikesh; Sheldon, Jennifer; Bauer, Luke A.; Bindschaedler, Vincent; Sugawara, Takeshi; Rampazzi, Sara (May 2023, ACM)
4. LiDAR-based Cooperative Relative Localization

   https://doi.org/10.1109/IV55152.2023.10186549  

   Dong, Jiqian; Chen, Qi; Qu, Deyuan; Lu, Hongsheng; Ganlath, Akila; Yang, Qing; Chen, Sikai; Labi, Samuel (June 2023, 2023 IEEE Intelligent Vehicles Symposium (IV))
5. High-flux single-photon lidar

   https://doi.org/10.1364/OPTICA.403190  

   Rapp, Joshua; Ma, Yanting; Dawson, Robin_M_A; Goyal, Vivek_K (January 2021, Optica)

   In time-correlated single-photon counting (TCSPC), photons that arrive during the detector and timing electronics dead times are missed, causing distortion of the detection time distribution. Conventional wisdom holds that TCSPC should be performed with detections in fewer than 5% of illumination cycles to avoid substantial distortion. This requires attenuation and leads to longer acquisition times if the incident flux is too high. Through the example of ranging with a single-photon lidar system, this work demonstrates that accurately modeling the sequence of detection times as a Markov chain allows for measurements at much higher incident flux without attenuation. Our probabilistic model is validated by the close match between the limiting distribution of the Markov chain and both simulated and experimental data, so long as issues of calibration and afterpulsing are minimal. We propose an algorithm that corrects for the distortion in detection histograms caused by dead times without assumptions on the form of the transient light intensity. Our histogram correction yields substantially improved depth imaging performance, and modest additional improvement is achieved with a parametric model assuming a single depth per pixel. We show results for depth and flux estimation with up to 5 photoelectrons per illumination cycle on average, facilitating an increase in time efficiency of more than two orders of magnitude. The use of identical TCSPC equipment in other fields suggests that our modeling and histogram correction could likewise enable high-flux acquisitions in fluorescence lifetime microscopy or quantum optics applications. 

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