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.

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.

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.