Improvement of Accuracy and Precision of the LiDAR System Working in High Background Light Conditions

Environmental CW range-resolved S-lidars with Si/InGaAs arrays: limitations and capabilities under sky background

In this paper, we discuss some features of open-path remote sensing inherent to CW range-resolved S-lidars (S comes from Scheimpflug) as a new, to the best of our knowledge, and promising class of laser instruments for environmental monitoring. In many remote-sensing applications, the accompanying skylight can degrade the sensitivity and overload the photodetectors, which is also very relevant for S-lidars with Si and InGaAs arrays. We paid special attention to the topical problem of predicting the limitations and potential of S-lidars in the VIS and SWIR spectral bands, where the sky background is particularly strongly affected. For this purpose, the index of immunity against external backgrounds as a quantitative indicator of S-lidars' potential insensitivity to the current skylight is introduced. Its evaluation is carried out by comparing the potentially achievable signal-to-noise ratios at the detector output in the presence and absence of external illumination. The detector response to the skylight in the photon-counting mode is normalized to appropriate parameters of the array in order to use dimensionless estimates in describing the variability of conditions. Characteristic spectral and dark-current-related features distinguishing the response of Si and InGaAs array detectors in the presence of background illumination are taken into account. It is then shown how to determine the minimum required full well capacity of the array in order to neglect the skylight contribution and ensure stable operation of S-lidars. The proposed methodology is aimed at providing a rationale for design solutions to expand the applicability of this promising type of remote sensors.

A Fast Cross-Correlation Combined with Interpolation Algorithms for the LiDAR Working in the High Background Noise

Processing speed and accuracy of measurements are important factors reflecting the performance quality of light detection and ranging (LiDAR) systems. This study proposed a fast cross-correlation (fCC) algorithm to improve the computation loading in the LiDAR system operating in high background noise environments. To reduce the calculation time, we accumulated cycles of the receiver waveform to increase the signal-to-noise ratio. In this way, the stop pulse can be easily distinguished from the background noise by applying the cross-correlation (CC) on the accumulated receiver waveform with the first start pulse. In addition, the proposed fCC combined with variant interpolation techniques: the parabolic (fCCP), gaussian (fCCG), cosine (fCCC), and cubic spline (fCCS) to increase the measurement accuracy were also investigated and compared. The experiments were performed on the real-time LiDAR system under high background light intensity. The tested results showed that the proposed method fCCP achieved 879 ns per measurement, 38 times faster than the original CC method combined with the same parabolic interpolation algorithm (CCP) 33.5 μs. Meanwhile, the fCCS method resulted in the highest accuracy/precision, reaching 5.193 cm/8.588 cm, respectively. These results demonstrated that our proposed method significantly improves the measurements speed in the LiDAR system operating under strong background light.