The polarization-based attitude and heading reference system (PAHRS) consisting of inertial navigation system (INS) and polarization sensor (PS) offers an effective solution for attitude and heading determination in the case of global navigation satellite system (GNSS) signal degradation. Its performance depends largely on the state estimation accuracy. In the existing work, the Kalman filtering (KF), as a low complexity scheme, is employed to achieve the state estimation of PAHRS. However, in practice, the performance of PAHRS could be affected by weather conditions and the maneuvering state of the vehicle. The accurate noise statistics of INS and PS is often encountered, which leads to a degradation of PAHRS. To improve the adaptability and accuracy of the system, in this article, we conduct a KF flow-based deep neural networks (KFDNNs), a real-time state estimator that learns from PS and INS data to carry out Kalman filter. In the constructed KFDNNs, deep neural networks (DNNs) are inserted into the flow of the KF to learn the optimal Kalman gain from PAHRS data, which we can retain data efficiency and interpretability of the classic algorithm while circumvents the dependency of the KF on knowledge of the noise statistics. Moreover, a two-stage training strategy consisting of warm-up training stage and task-oriented training stage is presented for the KFDNNs, which mitigate the gradient explosion caused by the unstable random initialization of KFDNNs while improve the flexibility of sequence length selection. Finally, the simulation and vehicle test are carried to verify the performance of PAHRS. The experimental results confirm the KFDNNs outperforms KF-based INS/PS method especially in complex weather and maneuvering scenarios.

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.Note to Practitioners—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.

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 2-D orientation. To address the limitation, a 3-D 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 3-D attitude is acquired independently using underwater downwelling radiance fields. The effectiveness of the proposed strategy is validated via experiments in both the water tank and open sea environments.

Polarization navigation is an emerging autonomous navigation technology suitable for unmanned underwater vehicles (UUVs). Nonetheless, light refraction poses challenges for underwater polarization navigation as it alters the direction and amplitude of the polarization electric vector (E-vector). In this article, we propose a novel underwater heading determination method with inertial navigation system/polarization sensor integration in consideration of light refraction. In view of light deflection and energy attenuation under refraction, the direction correction matrix and amplitude compensation factor are established for the E-vector. On this basis, the refracted E-vector is introduced into the heading measurement model, which can reduce modeling errors induced by refraction, thereby producing a better prediction of heading information. In addition, a dual-filter algorithm is constructed to handle fusion models with different characteristics, improving the reliability and efficiency of heading estimation. Numerical simulation is conducted to confirm the feasibility of the proposed method, while ocean navigation experiments on UUV are carried out to evaluate its accuracy.

Accurate and reliable polarization information is extremely important as heading cues in underwater low-light environments, especially in the presence of hybrid refraction/scattering dynamic effects. In this article, a bio-inspired antagonistic differential polarization algorithm (ADPA) is proposed for polarization perception, enhancing the resolution and contrast of polarization signals in underwater low-light environments. Starting from the analysis of disturbances in underwater polarization measurements, the wavelet denoising algorithm is introduced to extract the effective light intensity. Subsequently, the antagonistic differential measurement equation is derived by mimicking biological antagonistic differential mechanisms, improving the polarization contrast from orthogonal photoreceptors. The proposed ADPA exhibits the capability to effectively handle consistent interference while dampening time-varying nonlinear disturbances. On this basis, a bio-inspired navigation strategy using ADPA is presented for heading determination. The mapping relationship between underwater polarization information and spatial motion information is revealed. Underwater navigation experiments in real oceans are carried out to manifest the effectiveness of the investigated ADPA-based method. In comparison with existing polarization methods, the proposed method can significantly improve the reliability, adaptability, and robustness of underwater polarization-based navigation.

Solar-tracking is one of the key issues in underwater polarization navigation. Most existing studies are based on the Angle of Underwater Polarization (AoUP). As water depth increases, however, the AoUP is disturbed by multiple scattering, while the Degree of Underwater Polarization (DoUP) maintains a stable relative relationship. To address the less robustness of polarization navigation, a solar-tracking and integrated orientation method based on DoUP is proposed. In consideration of the refraction, the relationship between the solar position and DoUP is established based on the maximum Degree of Polarization. Moreover, an integrated navigation model is developed aided by the solar position. Results from the static experiment in water tank and dynamic sea trials demonstrate that the proposed method exhibits improved accuracy and robustness compared to the AoUP method. This article presents a potential approach to improve the adaptability of underwater autonomous orientation.

The polarization-based attitude and heading reference system (PAHRS) provides an effective solution for attitude and heading information acquisition. Its practical performance, however, will be degraded due to partial loss and/or occlusion of the optical signal. To improve the adaptivity of PAHRS, in this article, we establish a polarization-based tightly coupled model (PTCM) and propose a reliable fusion strategy for information extraction from the polarization sensor (PS) and inertial navigation system (INS). As compared to the existing PS/INS fusion model, the proposed PTCM directly adopt PS raw observations (polarized skylight intensity) to compensate for the accumulation errors of INS, thereby removing the constraints on the least number of PS observation channels and avoiding nonlinear transformation of PS noises. Moreover, the reliable fusion strategy consists of a reliable observation channel selection step followed by a nonlinear filtering step, which can reduce the effect of unreliable polarized skylight intensity measurement. Finally, the simulation, static and semi-physical vehicle-mounted tests confirm the effectiveness of the proposed PTCM and fusion strategy.