Xiaohan Guo

Bio-inspired imaging polarimeters have significant applications in the field of detecting the polarization state of skylights. The polarization detection principle of polarization detection units in polarimeters is mostly based on the Stokes parameter method. Using the Stokes parameter method, multiple linearly polarized lights modulated by the incident light need to be obtained. According to the polarization modulation method of the polarization detection unit, imaging polarimeters can be classified into time-division types, channel-division types, and division of focal-plane types. Different from the classification in previous studies, this review divides channel-division polarimeters into single-sensor channel-division and multi-sensor channel-division polarimeters, avoiding the confusion of concepts between aperture-sharing polarimeters and amplitude-sharing polarimeters in previous classifications. This review introduces the different ways of achieving polarization-state imaging through various bionic imaging polarimeters and expands on the advanced polarization detection unit structure design technologies based on the Stokes parameter method introduced in recent years, aiming to provide inspiration for bio-inspired imaging polarimeters used in navigation and positioning.

To address the issue of error accumulation arising from the continuous rotation of polarizers within the polarization rotation table, this paper proposes a computational methodology for determining the true rotation angle of polarizers in instances where the rotation angle is unknown. From obtaining atmospheric polarization data, we delve into the relationship between the incident and outgoing light as it traverses optical components, assuming knowledge of the initial polarization state. Furthermore, we expound on the transformational relation between Stokes vectors and Mueller matrices. We leverage these foundational theories to attain precise atmospheric polarization information for multiple pixels. To mitigate the cumulative errors arising from the continuous rotation of polarizers, we use a nonlinear least squares optimization approach to determine polarization rotation angles in the images accurately. Additionally, this innovative approach was compared with the Gauss-Newton method. Simulation results clearly show that, under identical conditions, the algorithm described in this paper surpasses the Gauss-Newton method regarding precision, iteration count, and efficiency. Experimental validation unequivocally affirms the efficacy of the proposed algorithm in precisely estimating polarization rotation angles in images, a pivotal component for the subsequent meticulous reconstruction of atmospheric polarization patterns.

A polarized light sensor is applied to the front-end detection of a biomimetic polarized light navigation system, which is an important part of analyzing the atmospheric polarization mode and realizing biomimetic polarized light navigation, having received extensive attention in recent years. In this paper, biomimetic polarized light navigation in nature, the mechanism of polarized light navigation, point source sensor, imaging sensor, and a sensor based on micro nano machining technology are compared and analyzed, which provides a basis for the optimal selection of different polarized light sensors. The comparison results show that the point source sensor can be divided into basic point source sensor with simple structure and a point source sensor applied to integrated navigation. The imaging sensor can be divided into a simple time-sharing imaging sensor, a real-time amplitude splitting sensor that can detect images of multi-directional polarization angles, a real-time aperture splitting sensor that uses a light field camera, and a real-time focal plane light splitting sensor with high integration. In recent years, with the development of micro and nano machining technology, polarized light sensors are developing towards miniaturization and integration. In view of this, this paper also summarizes the latest progress of polarized light sensors based on micro and nano machining technology. Finally, this paper summarizes the possible future prospects and current challenges of polarized light sensor design, providing a reference for the feasibility selection of different polarized light sensors.

The NN (neural network)-PSO (particle swarm optimization) method is demonstrated to be able to inversely extract the coating parameters for the multilayer nano-films through a simulation case and two experimental cases to verify its accuracy and robustness. In the simulation case, the relative error (RE) between the average layer values and the original one was less than 3.45% for 50 inverse designs. In the experimental anti-reflection (AR) coating case, the mean thickness values of the inverse design results had a RE of around 5.05%, and in the Bragg reflector case, the RE was less than 1.03% for the repeated inverse simulations. The method can also be used to solve the single-solution problem of a tandem neural network in the inverse process.