Polarization Navigation Research Articles

Multi-environment robust polarization navigation sensor and the parameter adaptive prediction method

As a type of photosensitive sensor, the polarization navigation sensor is susceptible to variations in external optical information. Experiments have demonstrated that light intensity is one of the factors influencing solution parameters. Considering that traditional polarization navigation sensor cannot detect light intensity effectively, a polarization sensor with light intensity detection capability is designed. Additionally, a neural network-based adaptive parameter method is proposed. By utilizing a neural network for the real-time solution degree of linear polarization (DLOP) and combining the light intensity information with polarization information from the new sensor, the method finally realizes the prediction of external solution parameters, thus enabling the solution model to adapt to the environmental variations. Finally, the effectiveness of the adaptive prediction method is verified by experiments. The results of outdoor experiments show that the adaptive prediction method reduces the mean square error (MSE) of angle of polarization (AOP) by approximately 42.11%, 44.78%, and 52.83% compared to the convention solution method under sunny, cloudy, and overcast conditions.

Reliable measurement selection mechanism-based tightly coupled inertial/bionic polarization integration with position correction

Tightly coupled SINS/BPNS (strapdown inertial navigation system/bionic polarization navigation system) integration has been proven to be a promising navigation tactic to substitute SINS/GNSS (global navigation satellite system) integration in GNSS-denied environments. However, the existing tightly coupled SINS/BPNS integrations lack SINS position correction and the reliable screening of BPNS measurements, leading to difficulty to complete navigation tasks. This paper presents an enhanced tightly coupled SINS/BPNS integration to correct both SINS position and attitude and conduct reliable measurement screening for BPNS. This method establishes a new measurement model by formulating the relationship between the SINS position error and angle of E-vector to effectively correct the SINS position. Based on the corrected SINS position, the solar position is calculated and further plugged in the measurement model, leading to improved accuracy. Further, based on the focal plane polarization camera, a mechanism of reliable measurement selection with two-level processing is developed and combined with the extended Kalman filter to improve the filtering robustness for tightly coupled SINS/BPNS integration. Results of simulations and experiments show that the proposed method not only can effectively correct SINS navigation information, but also can possess a strong robustness to exclude unreliable BPNS measurements, leading to enhanced navigation performance for tightly coupled SINS/BPNS integration.

Error analysis and calibration based on division-of-aperture bionic polarization navigation systems

Bionic polarization navigation technology has become an important navigation technology due to its non-dependence on satellite technology, and strong anti-jamming ability. However, in practical applications, the performance parameters of the structure of division-of-aperture polarization navigation systems limit the accuracy of polarization navigation. Therefore, this paper investigates the measurement error model and the corresponding calibration method for the multi-channel structure of division-of-aperture polarization navigation systems. In this paper, we first establish a mathematical model between the error of skylight polarization information and the performance parameters of navigation systems for the unique multi-channel structure of division-of-aperture polarization navigation systems. Secondly, for the first time, a mathematical model is used to quantitatively analyze and give the performance parameters suitable for the acquisition of skylight polarization information (polarizer installation errors ≤2°, grayscale non-uniformity ≤10(DN)). Then, a polarizer installation errors calibration algorithm based on Singular Value Decomposition (SVD) with linear fitting for grayscale non-uniformity response calibration algorithm is proposed and validated through indoor and outdoor calibration experiments. The experimental results show that the azimuth accuracy after calibration of polarizer installation errors can reach 0.382°, and the azimuth accuracy after calibration of grayscale non-uniformity response can reach 0.101°.

Ultraviolet bionic compass method based on non-ideality correction and statistical guidance in twilight conditions.

Bionic polarization compass is a fascinating subject in the navigation domain. However, the polarization navigation accuracy is severely degraded by the influence of city glow at dusk. Therefore, we proposed an ultraviolet bionic compass method based on non-ideality correction and statistical guidance. A non-ideal polarization imaging model was established to correct the system detection error. A meridian extraction algorithm based on the statistical properties of solar direction vectors was proposed for accurate heading calculation. The proposed algorithm was demonstrated experimentally and reduced the heading error to approximately 1°, which shows strong anti-interference performance against urban glare.

Design and simulated characterization of the dual-band polarizer based on metasurface structure

Dual-band polarizers must simultaneously exhibit high transmittance and bandwidth efficiency in fields such as polarization navigation and efficient detection. However, in most studies, the detection of dual bands is inefficient. To address this issue, we designed a dual-band polarizer similar in structure to the Roman numeral II, and it allows transmission of two different linear polarizations within different frequency bands. The interaction of the dual-band nano-polarizer with the polarization state of the incident light was analyzed through simulation experiments on the material, height, duty cycle, and other parameters of the periodic structure of the polarizer unit. The simulation results show that the proposed dual-band polarizer could achieve a polarization degree above 0.75 in the visible range and above 0.8 in the near-infrared (NIR) range. In addition, the transmittance was above 80% for x-polarization light in most of the visible wavelengths and reached 97.7% for y-polarization light in the NIR region, where NIR bandwidth accounted for 83.3% of the NIR wavelengths. The proposed design can achieve high transmittance and can be applied to ultrawide single-band polarization detection or dual-band vertical polarization detection.

Underwater Downwelling Radiance Fields Enable Three-Dimensional Attitude and Heading Determination

Underwater autonomous navigation has long been a challenging problem due to the scarcity of information sources. The polarization navigation offers a feasible solution to this problem. The existing polarization navigation schemes, however, require that the horizontal attitude is known, which can only be used for two-dimensional orientation. To address the limitation, a three-dimensional (3D) attitude determination strategy is developed in this article by exploiting the underwater downwelling radiance fields (light intensity and polarization). In particular, the horizontal attitude information contained in the Snell's window, a unique underwater optical phenomenon induced by refraction, is extracted via an improved edge recognition method. On this basis, underwater polarization is exploited to calculate solar position for orientation. By this means, the 3D attitude is acquired independently using underwater downwelling radiance fields. The effectiveness of the proposed strategy is validated via experiments in both water tank and open sea environments.

An Efficient Design Method for a Metasurface Polarizer with High Transmittance and Extinction Ratio

In fields such as polarization navigation and imaging, there is a demand for polarizers to simultaneously possess a high transmittance and extinction ratio. However, most studies focus solely on a single metric for polarizer design. To address this issue, this paper proposed a metasurface polarizer with a transition layer and its efficient design method, aiming to enhance the polarizers’ transmittance and extinction ratio performance simultaneously. An all-dielectric metasurface polarizer with a TiO2 transition layer was designed, and a tandem neural network was constructed by combining forward prediction and inverse design networks. The network is successfully trained by utilizing structural parameters and spectral response Tx and Ty datasets collected through simulation software. A high-performance metasurface polarizer was designed under the desired metrics with a transmittance of 90% and an extinction ratio of 40 dB. The proposed method in this paper made progress in the comprehensive optimization of polarizer performance. It is more accurate and efficient compared to other design methods and is better suited for practical applications.

Underwater autonomous orientation using submarine light intensity gradient

It is a huge challenge for autonomous underwater vehicles (AUVs) to determine heading autonomously without the signal of satellite or underwater acoustic positioning system. The underwater polarization navigation is a newly-developed solution for self-orientation. However, the polarization navigation depending on the Rayleigh scattering model is susceptible to the multiple scattering induced by water depth. To address this issue, the underwater light intensity field is exploited benefiting from its environment suitability. By means of calculating the gradient of light intensity, a vector-field model, with resemblance to the ideal Rayleigh model in geometry, can be established for solar-tracking. The integrated navigation model is built based on the solar vector estimated by light intensity gradient (LIG) and inertial navigation system for heading determination. The static test in water tank and dynamic sea trial were conducted to validate the effectiveness of the LIG-based solar-tracking and orientation method, respectively.

Image-registration-based solar meridian detection for accurate and robust polarization navigation.

Skylight polarization, inspired by the foraging behavior of insects, has been widely used for navigation for various platforms, such as robots, unmanned aerial vehicles, and others, owing to its stability and non-error-accumulation. Among the characteristics of skylight-polarized patterns, the angle of polarization (AOP) and the degree of polarization (DOP) are two of the most significant characteristics that provide abundant information regarding the position of the sun. In this study, we propose an accurate method for detecting the solar meridian for real-time bioinspired navigation through image registration. This method uses the AOP pattern to detect the solar meridian and eliminates the ambiguity between anti-solar meridian and solar meridian using the DOP pattern, resulting in an accurate heading of the observer. Simulation experiments demonstrated the superior performance of the proposed method compared to the alternative approaches. Field experiments demonstrate that the proposed method achieves real-time, robust, and accurate performance under different weather conditions with a root mean square error of 0.1° under a clear sky, 0.18° under an overcast sky with a thin layer of clouds, and 0.32° under an isolated thick cloud cover. Our findings suggest that the proposed method can be potentially used in skylight polarization for real-time and accurate navigation in GPS-denied environments.

A Novel Tightly Coupled Solution for SINS/Polarized Navigation System/Odometer Integration Using Polarized and Installed Angle Errors Model

Precise and reliable autonomous navigation in a GPS-denied environment is critical to unmanned systems. The idea of combining SINS, the polarized navigation system (PNS), and the odometer (OD) inspired by desert ants has been proven to be effective for autonomous navigation. However, there are two major challenges for polarization navigation nowadays: inaccurate modeling and obtaining reliable heading information when some sensor channels are blocked. Aiming at these two problems, a tightly-coupled solution for SINS/PNS is proposed in this paper. To obtain a refined integrated navigation system model, the installation errors between the inertial units and PNS, and polarization angle calculation errors are analyzed and modeled for SINS/PNS. Then, to quickly gain the accurate state estimation, an improved iterative unscented filtering method is devised. In particular, the sigma-point updating step with the conditional distribution of high-dimensional Gaussian distribution random variables is developed, which employs partial states to sample in each iteration to reduce the calculation burden. Finally, a detection and elimination mechanism for the abnormal light channels is provided to enhance the reliability of the integration in the presence of a light blockage. The optical channels for navigation are chosen using this mechanism depending on the difference between the predicted and measured incident light intensities. The results in both simulations and outdoor experiments show that the proposed method provides a higher heading estimation accuracy than the traditional SINS/PNS navigation method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the inaccurate modeling and obtaining reliable heading in the presence of light occlusion for the polarization navigation. Various integrated navigation algorithms based on polarized skylight have been widely developed. However, the complex environment and inaccurate modeling limit the application of the polarization navigation. This article gives a novel accurate modeling method and reliable navigation algorithm. Tightly-coupled model with installation errors and the polarization angle error aims to improve the accuracy of the polarization navigation model. The improved iterative unscented Kalman filter is utilized to quickly obtain accurate state estimation. And the light detection and elimination mechanism is used to select normal light channels to navigate in the presence of light blockage. This navigation method can also extend the application of the polarization navigation.

Self-Adaptive Image Segmentation Algorithm for Polarization Navigation under Complex Scenes

Self-Adaptive Image Segmentation Algorithm for Polarization Navigation under Complex Scenes

Study on the Polarization Pattern Induced by Wavy Water Surfaces

In nature, the wavy ocean surface is a common polarizer, which can change the polarization state of incident light by refraction and reflection and form a new polarization pattern different from the atmosphere. In this paper, we establish the polarized optical transmission model of wavy ocean surface reflection and refraction and simulate polarization patterns induced by wavy ocean surfaces. We study the polarization patterns reflected by wavy water surfaces and polarization patterns inside and outside Snell’s window under wavy ocean surfaces. The correctness of the simulation results is verified by qualitative and quantitative analysis. The environmental factors affecting the corresponding polarization patterns are discussed. Through contrastive analysis, we find that polarization patterns induced by wavy water surfaces are predictable and regular, which has great potential for human application. This kind of polarization pattern is influenced by the sun’s position and water surface condition. The study will promote the development of remote sensing, target detection, and polarization navigation.

Design of a real-time orientation sensor based on atmospheric polarization light.

In recent years, biomimetic polarization navigation has become a research hotspot in navigation fields because of its autonomy and concealment. Existing point-source polarization navigation sensors mainly use a logarithmic amplifier as the arithmetic unit to obtain polarization information. However, these sensors suffer from zero drift and low detection accuracy, which limits their application range. To address the above issues, a polarization navigation sensor based on a differential amplifier is designed as the operational unit. Based on the change of the arithmetic unit of the polarization signal, the algorithm for calculating the heading angle of the sensor is improved. The results of the orientation experiments with the designed sensor in clear weather indicate that the orientation error is ±1.243∘, and the standard deviation is 0.351°. The polarization navigation sensor can extract polarized light information and calculate the heading without accumulation of errors over time accurately and achieves good real-time performance.

Skylight Polarization Pattern Simulator Based on a Virtual-Real-Fusion Framework for Urban Bionic Polarization Navigation.

In a data-driven context, bionic polarization navigation requires a mass of skylight polarization pattern data with diversity, complete ground truth, and scene information. However, acquiring such data in urban environments, where bionic polarization navigation is widely utilized, remains challenging. In this paper, we proposed a virtual-real-fusion framework of the skylight polarization pattern simulator and provided a data preparation method complementing the existing pure simulation or measurement method. The framework consists of a virtual part simulating the ground truth of skylight polarization pattern, a real part measuring scene information, and a fusion part fusing information of the first two parts according to the imaging projection relationship. To illustrate the framework, we constructed a simulator instance adapted to the urban environment and clear weather and verified it in 174 urban scenes. The results showed that the simulator can provide a mass of diverse urban skylight polarization pattern data with scene information and complete ground truth based on a few practical measurements. Moreover, we released a dataset based on the results and opened our code to facilitate researchers preparing and adapting their datasets to their research targets.

A Bioinspired Navigation System for Multirotor UAV by Integrating Polarization Compass/Magnetometer/INS/GNSS

Stable and accurate heading angle estimation is challenging for low-cost small UAV navigation, especially in the presence of geomagnetic anomaly. In this paper, a bio-inspired integrated navigation system (BINS) is designed such that the environmental adaptability of the navigation system can be enhanced. A bio-inspired integrated navigation algorithm is presented to fuse the information of polarized skylight, geomagnetic field, inertia, and global navigation satellite system (GNSS). The relationship among ambient light intensity, degree of polarization, and accuracy of polarization compass is revealed. Thus, the integrated polarization compass can be adapted to more complex weather conditions. A tightly coupled integrated navigation model is developed for BINS to fuse the information of polarization compass and magnetometer. Within the developed scheme, the polarization compass can not only correct the heading error of the system, but also help the magnetometer reconstruct the actual local geomagnetic field distribution. The problem of inaccurate geomagnetic field model in the magnetic anomaly environment is addressed. Moreover, the Chi-Square test is used to detect polarization compass outliers such that the navigation modes can be switched promptly. Finally, the ground static experiments and UAV flight tests are carried out. In comparison of traditional polarization navigation and geomagnetic navigation, the proposed system effectively improves the heading accuracy.

Robust Heading Measurement Based on Improved Berry Model for Bionic Polarization Navigation

With the advantages of anti-interference and no accumulated error, bionic polarization heading measurement has important military significance and research value for autonomous navigation. However, the heading robustness is seriously affected by the accuracy of the skylight polarization model and the existing methods all use the Rayleigh scattering model without considering atmospheric depolarization. Therefore, this article proposed an innovative method of polarization heading measurement based on the Berry model to consider the influence of depolarization neutral points. And this model is improved by controlling the neutral points with correction coefficients to realize high-robustness heading measurement. On this basis, the improved Berry model is further used to calibrate the sensor parameters outdoors, which separates the skylight polarization model error from the sensor error without expensive instruments and complicated processes and improves the heading measurement accuracy. In the experiments with changing solar altitude, the average heading error STD after field calibration is 20.86% lower than that of the Berry model and 92.85% lower than that of the Rayleigh model, which shows great advancement in actual measurements.

The Research on Ocean Polarized Light Fields at Different Depths for Polarization Navigation

The Research on Ocean Polarized Light Fields at Different Depths for Polarization Navigation

Moonlit polarized skylight-aided INS/CNS: An enhanced attitude determination method

Autonomous navigation in complex environments (e.g., global navigation satellite system denied and unknown environments) is paramountly important for unmanned systems. Inspired by nocturnal insects’ navigation mechanism, an integrated navigation method combining the information of polarization navigation system (PNS), inertial navigation system (INS), and celestial navigation system (CNS) is presented for attitude determination at night. In order to improve the robustness of attitude estimation, a two-mode attitude determination system is designed, which includes the PNS/INS/CNS (PIC) mode and PNS/INS (PI) mode. With respect to the PIC mode, the bias in moon azimuth calculated by polarized skylight is modeled as a system state and subsequently estimated according to the precise starlight information. By detecting the CNS fault, the PIC mode is switched out. Focusing on PI mode, the estimated moon azimuth bias is applicable to improve the accuracy of the polarization information. A series of simulations and field tests are conducted to evaluate the performance of the presented method. The experimental data indicates that the presented method is able to achieve accurate and stable attitude estimation, which may offer valuable insight into the navigation of complex night environments.

Anisotropic charge trapping in phototransistors unlocks ultrasensitive polarimetry for bionic navigation

Being able to probe the polarization states of light is crucial for applications from medical diagnostics and intelligent recognition to information encryption and bio-inspired navigation. Current state-of-the-art polarimeters based on anisotropic semiconductors enable direct linear dichroism photodetection without the need for bulky and complex external optics. However, their polarization sensitivity is restricted by the inherent optical anisotropy, leading to low dichroic ratios of typically smaller than ten. Here, we unveil an effective and general strategy to achieve more than 2,000-fold enhanced polarization sensitivity by exploiting an anisotropic charge trapping effect in organic phototransistors. The polarization-dependent trapping of photogenerated charge carriers provides an anisotropic photo-induced gate bias for current amplification, which has resulted in a record-high dichroic ratio of >104, reaching over the extinction ratios of commercial polarizers. These findings further enable the demonstration of an on-chip polarizer-free bionic celestial compass for skylight-based polarization navigation. Our results offer a fundamental design principle and an effective route for the development of next-generation highly polarization-sensitive optoelectronics.

Preparation of an Integrated Polarization Navigation Sensor via a Nanoimprint Photolithography Process

Based on the navigation strategy of insects utilizing the polarized skylight, an integrated polarization sensor for autonomous navigation is presented. The polarization sensor is fabricated using the proposed nanoimprint photolithography (NIPL) process by integrating a nanograting polarizer and an image chip. The NIPL process uses a UV-transparent variant template with nanoscale patterns and a microscale metal light-blocking layer. During the NIPL process, part of the resist material is pressed to fill into the nanofeatures of the variant template and is cured under UV exposure. At the same time, the other parts of the resist material create micropatterns according to the light-blocking layer. Polymer-based variant templates can be used for conformal contacts on non-flat substrates with excellent pattern transfer fidelity. The NIPL process is suitable for cross-scale micro–nano fabrication in wide applications. The measurement error of the polarization angle of the integrated polarization sensor is ±0.2°; thus, it will have a good application prospect in the polarization navigation application.