Light detection and ranging (LiDAR) return signal generation technology applied in the LiDAR indoor test and simulation is significant to design, develop, test, and validate a LiDAR’s capability and performance. To generate a target’s information carried by the return signal, the dimensional decomposition and equivalent generation method of the LiDAR return signal are proposed. The target four-dimensional (4D) information is decomposed into one-dimensional (1D) intensity information, 1D range information, and two-dimensional (2D) angle–angle spatial information. The 1D intensity information is simulated by the absorption of prism pairs, while the 1D range information is simulated by the combination of electrical and optical time delay. The 2D angle–angle spatial information is implemented by the stack of segmented digital mirror array device slice images in sequence. Moreover, a LiDAR return scene projector (LRSP) prototype is developed and its performance is measured. The results show that its energy dynamic range is 51.25 dB. The distance simulation range is 240.15 m to 22.5 km (1.601 to 150 μs). The simulation accuracy of the target’s depth is <9 cm (0.6 ns). The spatial resolution of 64 × 64 pixels is verified by vertical and horizontal line pairs test. Because the LRSP has 12 image slices, its resolution is 64 × 64 × 12 pixels in three-dimensional (3D) space. Finally, the prototype is demonstrated by reconstructing a staircase. The energy dynamic and 2D angle-angle spatial resolution are improved significantly compared with the existing LRSPs.
Dynamic infrared scene projection is a technology for converting infrared (IR) digital image sequences into IR radiating image sequences. One of the most promising technologies is light down-conversion technology based on photoinduced opaque (PIO) effect. A parametric end-to-end steady-state model was proposed to describe the PIO mechanism. It consisted of three submodels such as the silicon parameter model, the photoinduced free carrier model, and the constitutive relation model. Furthermore, the parametric full-link from pump photons’ power density, photoinduced free carrier concentration, complex dielectric constant, and complex refractive index to the emissivity was constructed and mathematically analyzed by the above model. In order to verify the model, an intrinsic silicon wafer was pumped by a continuous-wave running Nd:YAG laser when the wafer was heated up to 321, 423, 459, and 498 K, respectively. Correspondingly, the emissivity integrated from 3 to 5 μm, with the increase of the pump power density measured by an IR camera. The measurement result agreed well with the theoretical result computed by the parametric end-to-end steady-state model. The maximum apparent temperature of the region illuminated by the laser with a pump power density of 407 W cm − 2 is up to 453 K when the silicon wafer was kept at 498 K. At the same time, the background temperature was elevated to 330 K owing to the initial free-carrier absorption enhancement.
A novel infrared small target detection algorithm based on potential regions proposal is proposed in this paper. Potential regions mean subsets (size are 16 by 16 in this paper) with small targets of an infrared image. A convolution neural network (CNN) classifier has been trained by using constructed datasets to discriminate potential regions of an input image. Traditional methods such as tophat transform, max-mean and max-median filter are used to suppress the background and noise of potential regions. Some experiments are carried out to verify the algorithm performance, and the results show that the gains of signal noise ratio and contrast ratio have better performance than traditional methods.