Stable and accurate heading angle estimation is challenging for low-cost small unmanned aerial vehicle (UAV) navigation, especially in the presence of geomagnetic anomaly. In this article, a bioinspired integrated navigation system (BINS) is designed such that the environmental adaptability of the navigation system can be enhanced. A bioinspired integrated navigation algorithm is presented to fuse the information of polarized skylight, geomagnetic field, inertia, and global navigation satellite system. 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 to traditional polarization navigation and geomagnetic navigation, the proposed system effectively improves the heading accuracy.

A novel autonomous initial alignment for the stationary strapdown inertial navigation system (SINS) with the bio-inspired polarized skylight sensors (PSS) is proposed to improve the precision and convergence speed in this paper. A pair of perpendicularly polarized skylight sensors with six-channels are used to measure and calculate the polarization azimuth angles, which is finally constructed a new polarized measurement error equation. With the SINS error equations, the SINS/PSS integrated navigation error equations are developed to achieve autonomous and fast initial alignment. Two types of observability, geometric observability and stochastic observability, are introduced to evaluate the integrated navigation systems. The geometric observability, which is defined by calculating the rank of the observability matrix, is analysed. It is found that the integrated navigation systems are not completely observable. Further, the degree of observability based on singular value decomposition is also used to examine the observable degree of the SINS/PSS integrated navigation system. The stochastic observability is associated with observability analysis with the estimated precision of the designed filter. The Kalman filter is designed to estimate the unknown state based on the measurements from the inertial measurement unit and PSS. Simulations and experiments demonstrate that the SINS/PSS achieve a rapid initial alignment with higher precision. Compared with the SINS, the vertical misalignment angle accuracy of SINS/PSS is improved by more than 90%, and the time cost is shortened by 60% in the simulation test. For the initial alignment experiment, the vertical misalignment greatly improved the accuracy and shorten the time cost.

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.

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.

Bionic navigation is an essential technology in GPS-denied environment for vehicle navigation. This paper combined strapdown inertial navigation system, polarized skylight sensors and odometer to design a new navigation model. In particular, a novel measurement model is developed based on polarized skylight. This model primarily utilizes the cross product to compute the error between the measured polarization vector and the theoretical polarization vector. It differs from the error computed by strapdown inertial navigation system in design vector measurement model. It deals with the problem of the directional ambiguity of polarization vector difference directly. Considering the measured polarization vector error due to computation and multiple scattering from atmospheric molecules, the states are augmented with the measured vector error to form a partially nonlinear, bionic integrated navigation model. To reduce the computation burden, marginalised unscented Kalman filter is presented to estimate the unknown states. The observability analysis method of piece-wise constant systems is applied to compute the observability matrix and singular value. Simulation results show that linear maneuver and angular maneuver can improve the degree of the observability of attitude and measured polarized vector error. Finally, experiments further verify the effectiveness of proposed models and method.

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.