This article proposes a set of novel attitude estimation algorithms for a bioinspired integrated navigation system including an insect-inspired polarization sensor, an inertial measurement unit, and a global navigation satellite system (GNSS). In particular, the biomimetic polarization sensor introduced as the new type of navigation component has an orientation performance comparable to that of a magnetometer. Moreover, this small and light sensor is immune to magnetic interference, making it highly suitable for orientation in unmanned aerial vehicles (UAVs). However, inaccurate modeling, unknown parameters, and unknown statistical characteristics of noise are common problems with the orientation solution of the sensor. Therefore, we theoretically compare various adaptive and robust filtering algorithms, select the best error prediction covariance and noise covariance correction schemes, introduce Kalman smoothers and approximate inference methods, and then propose two outlier-robust algorithms. This approach can suppress multiple outliers from the two steps of prediction and estimation. The polarization sensor and GNSS update the state and error covariance according to the asynchronous fusion mode. The prediction error covariance, the polarization measurement noise covariance, and the GNSS measurement noise covariance are approximately estimated by the variational inference method. To validate the performance of our algorithms, we collected a set of multi-rotor UAV flight data for testing. Experimental results show that the proposed algorithms have better robustness performance than existing state-of-the-art algorithms. Note to Practitioners —This article was motivated by Bioinspired autonomous navigation systems suffering from inaccurate modeling, unknown parameters, and noise with unknown statistical properties. Subject to the large uncertainty in the flight of the UAV, the traditional filtering algorithm has slow convergence or even divergence when processing such actual data. In this article, we first establish a unified state estimation framework. Then, we mathematically describe correction strategies for prediction error covariance and measurement noise covariance. Considering the need for real-time estimation, we adopted the extended Kalman filter. However, this introduces a truncation error, then, Rauch-Tung-Striebel smoothing is introduced to improve the linearized reference point. However, the role of robust filtering is limited, and the non-Gaussian state still exists. Finally, the approximate inference method is used to estimate the state. The proposed method integrates adaptability, robustness, reasoning, and smoothing, and is suitable for dealing with multi-source interference problems in multi-source information fusion.

In the past decades, the families of negative thermal expansion (NTE) materials have been in the uninterrupted growth with more new NTE materials reported; in particular, metal fluorides as the new members begin to draw attention. Herein, recent progress on the NTE properties of metal fluorides is reviewed, including compounds, mechanisms and the control of thermal expansion. Although some achievements have been made, there are still great development prospects. More in-depth investigations on metal fluorides with NTE behavior are expected

The near-zero temperature coefficient of resistivity (NZ-TCR) behavior is reported in the antiperovskite compounds Mn_3+xNi_1−xN (0 ≤ x ≤ 0.333). Our results indicate that the broad temperature range (above 275 K extending to above 220 K) of NZ-TCR is obtained by Mn doping at the Ni site. The short-range magnetic ordering is revealed by both neutron powder diffraction and inverse magnetic susceptibility. Further, we find a strong correlation between the anomalous resistivity change of Mn_3+xNi_1−xN from the metal-like to the NZ-TCR behavior and the lack of the long-range magnetic ordering. The possible mechanism of NZ-TCR behavior is discussed using the spin-disorder scattering model.

Underwater polarization (POL) fields contain spatial information of the sun. It offers underwater POL fields the capability of navigation and positioning. Benefiting from the advantages of autonomy, the POL positioning is an effective complement for the GNSS-denied underwater environment. The current POL-based positioning strategies always exploit the sun's position directly. However, the solution of geolocalization is susceptible to the constant deviations of sun measurement. In an effort to address this issue, a two-step positioning model is designed. The sun's positions over time are calculated by exploiting the POL field inside Snell's window in which the underwater disturbance can be shielded. Subsequently, the first step in the two-step model adopts time-sequence suns for coarse positioning. Around the coarse positioning solution, a local region is determined as the precondition for the second step positioning. A time differential sun (TDS) positioning strategy is proposed for a refined geolocation. TDS exploits the sun's dynamic movement for positioning, and thereby the position errors arising from the constant deviations are eliminated. An underwater POL positioning system is developed. The experimental results demonstrate that the positioning accuracy is improved distinctly by the TDS strategy.

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.

With the characteristics of full autonomy and no accumulated errors, polarisation navigation shows tremendous prospects in underwater scenarios. In this paper, inspired by the polarisation vision of aquatic organisms, a novel point-source polarisation sensor with high spectral adaptability (400 nm–760 nm) is designed for underwater orientation. To enhance the environmental applicability of the underwater polarisation sensor, a novel sensor model based on the underwater light intensity attenuation coefficient and optical coupling coefficient is established. In addition, concerned with the influence of light intensity uncertainty on sensor performance underwater, an antagonistic polarisation algorithm is adopted for the first time, to improve the accuracy of angle of polarisation and degree of polarisation in the low signal-to-noise ratio environment underwater. Finally, indoor and outdoor experiments are carried out to evaluate the performance of the designed polarisation sensor. The results show that the designed point-source polarisation sensor can acquire polarised light and be used for heading determination underwater.