Polarization Compass Research Articles

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.

Biomimetic enhanced polarization orientation method for underwater scenes

Underwater polarization information can provide reliable autonomous heading feedback for underwater scenes in the presence of satellite denial and multi-source interference. A biologically inspired highly robust polarization orientation strategy is proposed to address the degradation caused by weak polarization and noisy patterns in underwater environments with poor illumination, turbidity, and cloud recession. This approach initially develops a biomimetic algorithm that enhances weak polarization information, based on the increased perception mechanism of the visual neural pathway of syrphid fly under weak light conditions. After overcoming restricting polarization transmission, it is further optimized with a non-local sparse coding denoising part that takes into account the similarity in the detailed structure of the polarized image's effective zenith region. Relying on the stable ∞ symmetric distribution of the angle of polarization, the ±π/2 feature point distribution is traversed and assigned, and polarization information is quantitatively assigned, compensated, and regulated along the solar meridian region, excluding invalid polarized pixels. The experiments demonstrate that the methodology effectively reconstructs the symmetry of the partially damaged angle of polarization distribution, eliminates external interference, autonomously solves the fitted heading correction problem, and improves the environmental adaptability of the bionic polarization compass. The data from underwater experiments indicate the orientation accuracy: the error in weakly polarized patterns is within 2°, and the accuracy in noisy patterns can be increased by over 50%.

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.

Optimal vector matching fusion method for bionic compound eye polarization compass and inertial sensor integration

The compound eyes of insects can extract the heading by sensing multi-directional polarization information. Inspired by this, the integration with bionic eye polarization compass and low-precision MEMS inertial sensors yields a new choice for autonomous navigation in a GPS-denied environment. However, the occlusion environments, such as jungles and buildings, seriously affects the attitude estimation accuracy by destroying the polarization information. Considering the above problem, this paper proposes a reliable attitude estimation method based on vector matching measurement models for integration with bionic compound eye polarization compass and low-cost MEMS inertial sensors, including the polarization vector, solar vector, and gravity vector. To realize the matching and fusion among these vector models, the vector optimization selection algorithm is designed according to the number of visible polarization sensors in the occlusion environment. Particularly, the vector optimization selection factor is designed according to the error between the measured and theoretical polarization vectors. Furthermore, when using the solar vector estimation model, the degree of the polarization is applied to improve the calculation accuracy of the solar vector. The gravity vector measurement model is employed to enhance the autonomy of the integration. Finally, the effectiveness of the proposed method is verified by simulated and outdoor experiments under tree obscuration.

Robust Orientation Method Based on Atmospheric Polarization Model for Complex Weather

Accurate and reliable navigation data is a key component in Internet of Things (IoT). High-precision and stable autonomous orientation has attracted considerable attention regarding environments in which the global navigation satellite system signal is unreliable. This study proposed a robust orientation method based on the atmospheric polarization mode called weather weighting sparse coding that includes weather classification, sparse coding, and fitting. In comparison with previous studies, the proposed method is effective in restoring the angle-of-polarization image and achieving accurate orientation under complex weather. Specifically, to achieve high-precision orientation in complex weather, a polarization compass was designed, which used the characteristics of the different levels of destruction of atmospheric polarization images under different weather conditions for classification and denoising. The proposed strategy was used to process atmospheric polarization images obtained in complex weather. Experimental results showed that the orientation accuracy was better than 0.31° (root-mean-square error) under conditions of overcast, sandstorms, clear with overexposed areas, and smog with tree-obscured and overexposed areas.

An Optimized Heading De-Noising Method of the Polarization Compass for Improving Orientation Accuracy

In recent years, the polarization compass (PC) has been extensively applied in the field of autonomous navigation due to its strong autonomy and anti-interference characteristics. Nevertheless, the existing research shows that the PC with high extinction ratio polarization sensor still offers poor orientation accuracy since there is a great quantity of noise in the heading data output from the compass. Here, we present a novel and promising scheme to denoise heading data to improve deficiency in heading data denoising of existing research. The purpose of this study aims to solve the problem that the orientation accuracy of PC caused by noise needs to be improved. Concretely, the major points of this research include an effective separation strategy of noise and useful signal based on the optimized variational mode decomposition (OVMD) and an effective heading data denoising method based on an adaptive singular spectrum analysis (AD-SSA). OVMD based on the center frequency is employed to decompose the original noisy heading data into a finite number of band-limited data intrinsic mode functions (DIMFs) so that the best number of decomposed layers for the noising data can be obtained. Subsequently, the above decomposed DIMFs are divided into low-frequency signal components, mixed components, and high-frequency noise components exploiting the maximum similar eigenvalue of the autocorrelation matrix corresponding to each DIMF as a classification method. An AD-SSA uses the correlation coefficient to further suppress the noise in classified DIMFs. These approaches are combined effectively to improve the orientation accuracy of the PC significantly and very contribute to the navigation application of unmanned mobile platforms such as unmanned aerial vehicles (UAVs). Compared to existing prior arts, the effectiveness of the proposed heading denoising scheme for the PC is demonstrated through the results obtained from the heading data sets on two representative sunny and dust days.

Angle of polarization calibration for omnidirectional polarization cameras.

Polarization cameras quantify one of the fundamental properties of light and capture intrinsic properties of the imaged environment that are otherwise omitted by color sensors. Many polarization applications, such as underwater geolocalization and sky-based polarization compass, require simultaneous imaging of the entire radial optical field with omnidirectional lenses. However, the reconstructed angle of polarization captured with omnidirectional lenses has a radial offset due to redirection of the light rays within these lenses. In this paper, we describe a calibration method for correcting angle of polarization images captured with omnidirectional lenses. Our calibration method reduces the variance of reconstructed angle of polarization from 76.2 ∘ to 4.1 ∘. Example images collected both on an optical bench and in nature, demonstrate the improved accuracy of the reconstructed angle of polarization with our calibration method. The improved accuracy in the angle of polarization images will aid the development of polarization-based applications with omnidirectional lenses.

Confocal Ellipse Hough Transform for Polarization Compass in the Nonideal Atmosphere

The polarization compass is an autonomous, long-endurance compass technique that is intended for enhancing the environmental suitability of the integrated navigation systems. Numerous research on polarization compass is based on the single Rayleigh scattering model. However, the real atmosphere is usually non-ideal and does not conform to the ideal single Rayleigh scattering model. The single Rayleigh scattering model would have a constant error in a non-ideal sky with a certain partial cover. Compared with the single-scattering model, Hannay’s model with multi-scattering equations provides a better description of the polarization pattern in the real atmosphere. Thus, the ellipse Hough transform algorithm(EHT) is proposed in this study to extract neutral points from the polarization pattern based on Hannay’s model to correct the error mentioned above. To prove the effectiveness of ellipse Hough transform algorithm, several experiments have been carried out in the non-ideal sky. And experiment results show that the average error of solar zenith and azimuth angle of the Hough transform is less than 1° in non-ideal weather with 30% cover. In conclusion, the ellipse Hough transform improves the accuracy of the polarization compass by 50% in multi-scattering light with a partial coverage of the sky and enlarges the environmental suitability of the polarization compass.

Robust Adaptive Heading Tracking Fusion for Polarization Compass With Uncertain Dynamics and External Disturbances

For the polarization compass (PC), maintaining high-precision and high-robustness autonomous measurement of the heading information under conditions where the atmospheric polarized light is being blocked or disturbed represents a challenging problem. In this paper, the E-vector orientation algorithm, which provides high orientation accuracy and real-time performance in sunny weather, and the symmetric axis orientation algorithm, which offers a strong anti-interference ability in occluded environments, are integrated to solve this problem. Furthermore, an improved state error model is established to increase the accuracy of the heading estimation. To track the system state rapidly and circumvent the uncertainties caused by measurement noise variance, an improved variational Bayesian strong tracking cubature Kalman filter (VBSTCKF) is proposed to estimate both the state and the time-varying measurement noise variance accurately. In addition, a multi-frequency data fusion algorithm based on residual compensation is proposed to overcome the inconsistent sampling operation frequency problems of the two orientation algorithms. The outstanding feature is that the proposed multi-frequency VBSTCKF (MF-VBSTCKF) can improve the orientation precision and robustness while also ensuring the high-frequency output performance. Finally, experimental results acquired in occluded environments verify the superiority of the proposed method.

Magnetoreception in mammals and birds: a comparison (Mammalia, Aves)

The magnetic compass systems of birds and mammals differ in their functional modes and are based on different physical principles: The inclination compass of birds is not sensitive to polarity; it is light-dependent; with the direction indicated by spin-chemical processes in the photo-pigment cryptochrome. The polarity compass of mammals works also in total darkness and is based on magnetite, a biogenic iron-containing substance. Aside from the compass, birds include magnetic components in their navigational ‘map’; these components are based on magnetic intensity and are perceived by magnetite-containing receptors. Mammals probably also have a ‘map’, but its components are unclear. Reception based on magnetite particles appears to be the primary form of sensing the magnetic field, which, in the course of evolution, developed in different ways in the various animal groups: in mammals into a compass system to determine directions and in birds into parts of the ‘map’ to determine position.

Multiscale Parallel Heading Error Processing Model for Polarization Compass

A multiscale parallel processing model of heading error for the polarization compass (PC) is developed in this article. Existing state-of-the-art schemes of raising heading measurement precision are implemented relying on not only ideal assumption in noiseless heading output from the PC but also the polynomial method employed to calculate the heading error caused by the varied attitude angles of PC, which leads to fall sharply in the PC heading accuracy in real applications. Moreover, the aforementioned denoising and heading error compensation are conducted independently. Here we specifically concentrate on a novel and promising parallel processing model for heading error as a way to improve the PC heading accuracy, where a comprehensive analysis of heading error sources is first carried out for the PC. Subsequently, the proposed multiscale parallel error processing model is presented to improve heading accuracy for the PC by repressing noise as well as compensating heading errors that are implemented synchronously. Specifically, variational mode decomposition (VMD) is exploited to decompose the original heading data output from PC into multiple intrinsic mode functions, which characterizes binned modes (BIMFs) with high-to-low frequencies by seeking the ensemble of the decomposed modes and their respective center frequencies. An adaptive multiscale principle component analysis (MS-PCA) scheme denoising approach exploiting the principle contribution rate is performed to attenuate the noise in the high-frequency BIMFs. An effective compensation approach with a long short-term memory (LSTM) modeling is introduced straightforward to compensate for the heading error for the low-frequency BIMF concurrently. The rotational test is carried out to verify that the heading error processed by VMD-PCA denoising and LSTM compensating, which are the most effective among the comparison algorithms and the RMSE of 0.3505 and 0.2668, increased by 80.10% and 82.00%, respectively. The UAV flight test demonstrates that the RMSE of the parallel scheme using VMD-PCA and LSTM performed simultaneously is the minimum values of 0.1282 and 71% higher than the original navigation error. It can be seen from various experimental results that the proposed multiscale parallel processing model of denoising and error compensation procedure performed synchronously shows attractive performance gains with respect to emerging single processing in terms of increasing the accuracy of the PC heading measurement.

Attitude-Induced error modeling and compensation with GRU networks for the polarization compass during UAV orientation

The polarization compass used for unmanned aerial vehicle (UAV) navigation is usually hypothesized to be arranged horizontally in conventional heading measurement, which leads to a noticeable heading error due to the inevitable tilts called the pitch angle and the roll angle during UAV flight process. In addition, we found that the coupling of the angle between the solar meridian and the body axis of a carrier (A-SMBA) and tilted-angles will produce more remarkable heading errors. Consequently, we first introduce a comprehensive analysis of heading error in terms of variable attitude angles of the compass including the A-SMBA, the pitch angle and the roll angle. A novel heading error modeling and compensation for attitude-changed of the polarization compass by gated recurrent unit (GRU) neural network is developed subsequently. The experimental results demonstrate the proposed heading error modeling and compensation method performs best compared to state-of-the-art algorithms in predicting the UAV orientation.

Multi-rate strong tracking square-root cubature Kalman filter for MEMS-INS/GPS/polarization compass integrated navigation system

The microelectromechanical system-inertial navigation system (MEMS-INS) is one of the most widely used sensor systems in navigation but must be used with other sensors because of its error accumulation characteristics. The information fusion method for integrated navigation systems based on filtering technology is thus very important. This paper introduces a new adaptive Kalman filter for nonlinear integrated systems. An improved multi-rate strong tracking square-root cubature Kalman filter (MR-STSCKF) for a MEMS-INS/Global Positioning System (GPS)/polarization compass integrated navigation system is proposed. The proposed filter is used to estimate the system covariance adaptively. The proposed approach can overcome the problem of the inconsistency between the sampling frequencies of different sensors while maintaining the high precision of the integrated navigation results. Experimental results demonstrate that the proposed MR-STSCKF algorithm is effective in improving the accuracy of the MEMS-INS/GPS/polarization compass integrated navigation system with a high sampling frequency.

Bionic Integrated Positioning Mechanism Based on Bioinspired Polarization Compass and Inertial Navigation System.

In this paper, to address the problem of positioning accumulative errors of the inertial navigation system (INS), a bionic autonomous positioning mechanism integrating INS with a bioinspired polarization compass is proposed. In addition, the bioinspired positioning system hardware and the integration model are also presented. Concerned with the technical issue of the accuracy and environmental adaptability of the integrated positioning system, the sun elevation calculating method based on the degree of polarization (DoP) and direction of polarization (E-vector) is presented. Moreover, to compensate for the latitude and longitude errors of INS, the bioinspired positioning system model combining the polarization compass and INS is established. Finally, the positioning performance of the proposed bioinspired positioning system model was validated via outdoor experiments. The results indicate that the proposed system can compensate for the position errors of INS with satisfactory performance.

Seamless integration of polarization compass and inertial navigation data with a self-learning multi-rate residual correction algorithm

Yaw angle measurement by inertial navigation systems (INS) is of fundamental importance for military and civilian applications of unmanned ground vehicles. To reduce the INS accumulated error, INS and other navigation methods are commonly combined through information fusion schemes such as Kalman filters. As well, yaw angle measurement can be realized by bionic-vision navigation tools, especially the polarization compass (PC). However, the realization of PC/INS integrated navigation systems is restricted by differences of the PC and INS sampling frequencies, as well as the PC environmental disturbances. In this paper, a cubature Kalman filter with a multi-rate residual correction algorithm is proposed to fuse INS and PC navigation outputs with different sampling frequencies. As well, a long short-term memory network is employed for PC output prediction and fusion when actual PC data is not available. The performance of the proposed method has been experimentally verified using PC and INS data for different trajectories of ground-vehicle navigation tests. Comparative results of the proposed and traditional methods show a superior accuracy of the proposed method for the yaw angle measurement fusion even when the PC is temporarily unusable.

Design and calibration model of a bioinspired attitude and heading reference system based on compound eye polarization compass

Insects such as honeybees are capable of fusing the information sensed by multiple sensory organs for attitude and heading determination. In this paper, inspired by the sensory fusion mechanism of insects’ polarization compass and haltere, a bioinspired polarization-based attitude and heading reference system (PAHRS) is presented. The PAHRS consists of compound eye polarization compass and inertial measurement unit (IMU). By simulating multi-view structure of the dorsal rim area in insects’ compound eyes, a non-coplanar ‘polarization-opponent (POL)-type’ architecture is adopted for the compound eye polarization compass. The polarization compass has multi-directional observation channels, which is capable of adaptively selecting the angle of polarization and obtaining the polarization vectors. Therefore, the environmental adaptability of the polarization compass can be enhanced. In addition, the integration strategy between the compound eye polarization compass and IMU is proposed. Moreover, the sources of system errors are analyzed to improve the heading angle accuracy, based on which a new calibration model is established to compensate the installation errors of the PAHRS. Finally, experiments are carried out under both clear sky and cloudy conditions. The test results show that the error root mean square of heading angle is 0.14° in clear sky, and 0.42° in partly cloudy conditions.

Matched-filter coding of sky polarization results in an internal sun compass in the brain of the desert locust

Many animals use celestial cues for spatial orientation. These include the sun and, in insects, the polarization pattern of the sky, which depends on the position of the sun. The central complex in the insect brain plays a key role in spatial orientation. In desert locusts, the angle of polarized light in the zenith above the animal and the direction of a simulated sun are represented in a compass-like fashion in the central complex, but how both compasses fit together for a unified representation of external space remained unclear. To address this question, we analyzed the sensitivity of intracellularly recorded central-complex neurons to the angle of polarized light presented from up to 33 positions in the animal's dorsal visual field and injected Neurobiotin tracer for cell identification. Neurons were polarization sensitive in large parts of the virtual sky that in some cells extended to the horizon in all directions. Neurons, moreover, were tuned to spatial patterns of polarization angles that matched the sky polarization pattern of particular sun positions. The horizontal components of these calculated solar positions were topographically encoded in the protocerebral bridge of the central complex covering 360° of space. This whole-sky polarization compass does not support the earlier reported polarization compass based on stimulation from a small spot above the animal but coincides well with the previously demonstrated direct sun compass based on unpolarized light stimulation. Therefore, direct sunlight and whole-sky polarization complement each other for robust head direction coding in the locust central complex.

Global Autonomous Positioning in GNSS-Challenged Environments: A Bioinspired Strategy by Polarization Pattern

How to determine the absolute position of users, such as underwater autonomous vehicles, ships or other long-range vehicles, is a fascinating and challenging issue, especially in global navigation satellite system -denied environments. In this article, a bioinspired global autonomous positioning system based on polarized skylight is proposed. By comparing with the related literatures, the system is able to global positioning based on the degree of polarization pattern (DoP) pattern. To be more specific, in an attempt to use the DoP pattern for the geolocalization purposes, a bioinspired polarization compass is designed and in addition, an overall maximum DoP (MDoP) determination algorithm is presented. Moreover, on the basis of the MDoP value, the sun elevation angle can be ultimately measured by using the DoP pattern. The sun elevation angle difference algorithm is used to deduce the absolute position of users. Finally, the bioinspired global autonomous positioning system is developed and validated through experiments. The test results show that the location errors are in range of few kilometers, of which the latitude accuracy is 0.113° (1σ), and the longitude accuracy is 0.082° (1σ).

Comprehensive Heading Error Processing Technique Using Image Denoising and Tilt-Induced Error Compensation for Polarization Compass

Bionic polarization navigation has a broad variety of application in diverse fields for high reliability and strong robustness to interference, fundamental to which is the use of a polarization compass based on polarized light cues. Nevertheless, dramatical reduction of the orientation accuracy resulted from the noise in a measured angle of polarization (AoP) and the tilted angles of a polarization compass during operation gives imperative influence on navigation precision. Herein, we investigate how to improve the navigation accuracy effectively by the proposed comprehensive heading error processing technique for a polarization compass, where a novel denoising scheme is designed to eliminate the noise in AoP images directly by integrating the strength of iterative variance-stabilizing transformation (IVST) and adaptive soft interval thresholding (SIT) so as to compensate the following tilt-induced error accurately. Subsequently, a promising compensation approach inspired by efficient extreme learning machine (EELM) is introduced to correct the tilt-induced error caused by realistic execution. The AoP image denoising advance and the tilt-induced error modeling advance combine to produce remarkable performance gains on the heading error. Experimental results and comparisons with prior arts reveal that the proposed comprehensive heading error processing technique is highly appealing in terms of improving the orientation accuracy for a polarization compass with superiority to state-of-the-art alternatives.

Method and Implementation of a Bioinspired Polarization-Based Attitude and Heading Reference System by Integration of Polarization Compass and Inertial Sensors

How to self-determine the attitude and heading of users is a challenging issue in unknown, global navigation satellite system (GNSS) denied, or magnetic disturbance environments. In this article, a bioinspired polarization-based attitude and heading reference system (PAHRS) aided by inertial sensors is presented. As compared with the existing studies, a polarization compass, which consists of three photoelectric-based polarization sensors is designed to cope with the three-dimensional polarization heading determination and distinguish issue. Hence, the real heading orientation can be determined without GNSS correction. To address the triaxis attitude and heading determination issue of PAHRS, the system measurement model tightly coupled the attitude and heading accumulate errors of inertial navigation system. Finally, the experimental platform is built and the results illustrate that the PAHRS is able to achieve satisfactory performance without the aid by GNSS and magnetic compass system.