Geometric moment patterns are selected as non-orthogonal gray patterns used for single-pixel centroid detection. By using geometric moment patterns for structured illumination, the reflected light intensities collected by a single-pixel detector correspond to the zero-order and first-order geometric moments, which are leveraged to reconstruct and track the centroid of moving objects. However, the frame rate of the single-pixel tracking system is limited because geometric moment patterns are grayscale while the digital micromirror device (DMD) performs binary modulation much faster than grayscale modulation. Although spatial dithering addressed the problem by binarizing the patterns, the accuracy of tracking a small-sized moving object will decrease by the extra dithering error. Therefore, we propose a binary defocus method to improve the accuracy of tracking a small-sized moving object while ensuring a high frame rate. The key to the proposed method is to simulate grayscale geometric moment patterns by defocusing projection binary geometric moment patterns. The experimental results show that the proposed defocus method is still available for tracking small-sized moving objects even when the focused method fails, and ∼48% lower Centroid Estimation Error (CEE) than that of the focused approach when the frame rate is ∼7400 Hz. Meanwhile, our works also provide relationships between CEE of focused and defocus methods varies with the size of the moving object.

Single-pixel imaging lidar is a novel technology that leverages single-pixel detectors without spatial resolution and spatial light modulators to capture images by reconstruction. This technique has potential imaging capability in non-visible wavelengths compared with surface array detectors. An avalanche photodiode (APD) is a device in which the internal photoelectric effect and the avalanche multiplication effect are exploited to detect and amplify optical signals. An encapsulated APD detector, with an APD device as the core, is the preferred photodetector for lidar due to its high quantum efficiency in the near-infrared waveband. However, research into APD detectors in China is still in the exploratory period, when most of the work focuses on theoretical analysis and experimental verification. This is a far cry from foreign research levels in key technologies, and the required near-infrared APD detectors with high sensitivity and low noise have to be imported at a high price. In this present study, an encapsulated APD detector was designed in a linear mode by integrating a bare APD tube, a bias power circuit, a temperature control circuit and a signal processing circuit, and the corresponding theoretical analysis, circuit design, circuit simulation and experimental tests were carried out. Then, the APD detector was applied in the single-pixel imaging lidar system. The study showed that the bias power circuit could provide the APD with an operating voltage of DC 1.6 V to 300 V and a ripple voltage of less than 4.2 mV. Not only that, the temperature control circuit quickly changed the operating state of the Thermo Electric Cooler (TEC) to stabilize the ambient temperature of the APD and maintain it at 25 ± 0.3 °C within 5 h. The signal processing circuit was designed with a multi-stage amplification cascade structure, effectively raising the gain of signal amplification. By comparison, the trial also suggested that the encapsulated APD detector and the commercial Licel detector had a good agreement on the scattered signal, such as a repetition rate and pulse width response under the same lidar environment. Therefore, target objects in real atmospheric environments could be imaged by applying the encapsulated APD detector to the near-infrared single-pixel imaging lidar system.

Temperature is an extremely important meteorological parameter in atmospheric research. Accurate measurements of it are of profound significance for studying global greenhouse effects, meteorological forecasting, and other related aspects. Processing and statistical analysis were conducted on the temperature data observed throughout 2021 by the self-developed L1000 Rayleigh lidar. Comparison analyses were subsequently carried out with data from the U.S. Standard Atmosphere Model and satellite observations, revealing good consistency in trend and magnitude between the lidar and both counterparts. Statistical analysis of the time–space evolution characteristics of the atmospheric temperature of the vicinity of the Hefei area was performed at night, in the month, and in the quarter. We found the monthly average atmospheric temperature exhibits a distinct altitudinal structure of time–space change, with the highest temperature being spatially located in the 48–51 km region and the temperature range being 240–272 K. Within the temperature rise range for 20–49 km, temperature profiles across different months also exhibit consistent trends. Temporally, temperatures are lowest in October and highest in August, gradually rising from March to August and declining from August to October, with a slight increase from October to November. Compared to monthly temperature profiles, seasonal temperature profiles exhibit stable variation trends, with the highest temperatures observed in the 49–50 km region, ranging from 251–270 K. Spring, autumn, and winter temperature profiles intertwine, while the summer temperature profile notably surpasses those of the other three seasons, demonstrating a distinct seasonal variation trend.