Solar direct pumped solid-state lasers (SDPSSL) utilize solar energy as the pump source to directly convert sunlight into lasing output. These lasers have advantages such as simple structure, stable performance and being environmental friendly, which have great potential for applications in scientific research, industry and national defense. At first, the working principles and gain materials of solar direct pumped solid-state lasers are introduced. And then, the laser output power characteristics of solar directly pumped solid-state lasers are mainly reported at three different development stages. At last, the problems of current solar direct pumped solid-state lasers are analyzed, and the development prospects of the laser are discussed.

Sweep frequency laser source is widely used in optical fiber sensing, computing power network and 50G PON access network, in order to achieve a high quality O-band continuous tunable laser source, the 1 310 nm sweep frequency laser source based on Littman-Metcalf feedback configuration is reported. In this experiment, a gain chip with the reflectance of 0.005% of the curved waveguide end is used as the gain area of the inner cavity, the injection locking of the diffractive laser beam is realized through the external-cavity feedback technology. Experimental results show that under the condition of 500 mA injection current, the tunable wavelength range of 1 270~1 350 nm can be realized with mode-hopping free, the corresponding peak output power is 17.98 dBm, and the sweep power repeatability is about ±0.023 dB.

A theoretical model of temporal coherent stacking of short pulse based on Gires-Tournois interferometer (GTI) has been established. The progress of coherent pulse stacking and its temporal evolution is simulated theoretically. The impact of amplitudes and phases in the pulse burst on the stacking shape and efficiency is simulated numerically and analyzed. Through the simulation and optimized design of the amplitudes and phases in the pulse burst, the peak amplification coefficients of three pulses and five pulses in the coherent stacking are approaching 2.479 and 4.43, and the energies in the main pulse are 96.9% and 95.1% respectively.

A theoretical model for designing a surface grating compressor is established, and key physical parameters including grating constant, incident angle, dispersion and grating size are clarified. Using ray-trace method, a sort of nanosecond (ns) level surface grating compressors is designed, which adopts a single surface grating and a four-way transmission structure. The compressor has advantages such as large dispersion adjustable range, high diffraction efficiency, compact structure and strong power tolerance. The ns level four pass transmission surface grating compressor has a diffraction efficiency of 73.2%, reaching 94% of the theoretical value, and an output pulse width of 459 fs.

Aiming at the need for rapid sensing of target terrain in a large range of complex natural object environments in the wild, mainly in mountains and suburbs, an efficient 3D environment terrain segmentation technology is proposed, which combines the point algorithm based on Patch Work, the target point cloud clustering algorithm based on curved-voxel clustering (CVC), the semantic segmentation algorithm based on image and the terrain and the terrain segmentation algorithm based on image and laser fusion. Based on this technology, the advantage of information fusion between laser radar and visible light sensor is used to solve the problem of selecting ground objects in the all-day working state. Though technology development and application integration, the technology scheme proposed can cooperate with the photoelectric system to detect the ground object quickly and efficiently in the complex environment.

As an important component of the composite-axis photoelectric tracking system, the upper limit of the tracking accuracy of the system is determined by the perturbation suppression ability and dynamic response ability of the fast steering mirror itself. In order to improve the performance of the fast steering mirror system, a fractional-order active disturbance rejection controller based on the existing active disturbance rejection control theory and combined with the fractional-order theory is proposed. The design process of the controller is given, and the control effect of two control strategies to the fast steering mirror performance, i.e. the traditional active disturbance rejection controller (ADRC) and the fractional-order active disturbance rejection controller composed of the expanded state observer and fractional-order controller, is compared and analyzed through simulation and experimental verification. Experimental results show that the fractional-order active disturbance rejection controller improves the fast steering mirror rapidity by 20.58% compared with the traditional active disturbance rejection controller in the case of step response, while the fast steering mirror tracking accuracy improves by 26.9% in the case of sinusoidal tracking.

The visible light small target detection and recognition technology based on deep learning algorithm is an important field of academic research. Small targets account for a small proportion of all targets, have unclear features, and are difficult to detect. Firstly, in order to achieve fast and accurate recognition of small visible light targets, a deformable convolutional network is used based on the YOLOv8 network model to adjust the shape and size of the receptive field adaptively, enhancing the network’s ability to learn the invariance of complex targets. Secondly, an attention module is added to enable the model to focus on important information and improve its robustness. Finally, a specific small target dataset is constructed for training and validation, and the results show that the improved model has high accuracy and recall for specific targets.

Aiming at the problem of poor adaptability of existing lane detection algorithms, a new lane detection algorithm for complex traffic scenes is proposed. Firstly, a lightweight feature pyramid encoding network (FPENet) is used to achieve the semantic segmentation of lane lines to make a good trade-off between accuracy and speed. Secondly, the lightweight Mean shift clustering algorithm is used to cluster the mask lane lines to reduce the calculation cost. Finally, the improved random sample consensus (RANSAC) algorithm is used to implement the lane segmentation. Experimental results show that the algorithm can adapt to various complex road scenes, and the detection accuracy on the self-produced Tusimple extended data set reaches 97.64%, and the detection speed of single frame image reaches 50 frames per second, meeting the detection requirements of accuracy and real-time.