Accurately obtaining the navigation information of the device is crucial for realizing various emerging Internet of Things (IoT) applications, and a multi-source fusion navigation system is the key to achieving this goal. A distributed integrated inertial navigation system (INS), polarization compass (PC), and geomagnetic compass (MAG) enhanced direction approach is presented to improve the accuracy and robustness of the multisource fusion navigation system in complex environments. To estimate the time-varying measurement noise covariance in a nonlinear multi-source fusion navigation system, the traditional federated Kalman filter (FKF) is improved. In the FKF framework, the third-order spherical radial cubature rule and variational Bayesian theory are introduced, and a variational Bayesian federated cubature Kalman filter (VBFCKF) is proposed. Furthermore, a distributed information monitoring and compensation algorithm based on residuals is developed to address issues like anomalous measured values and asynchronous multi-rate problems. Finally, an experimental platform for unmanned vehicle navigation is designed, and the tests are conducted to confirm the efficacy of the suggested approach. The experimental results show that the system can precisely estimate values based on the measurement quality of sub-filters during navigation. It effectively adjusts measurement noise covariance during updates, thereby mitigating the negative impact of interferences like occlusions and electromagnetic noise on the multi-source fusion navigation system in complex environments. This can strengthen the accuracy and robustness of the navigation system.

Autonomous velocity measurement technology based on optical flow plays an important role in applications of the Internet of Things. However, robust velocity measurement results need to provide a robust optical flow field and distance to the ground, especially for complex ground scenes. To solve this problem, the present paper proposes a visual velocity measurement method based on a 3 × 3 camera-array multi-aperture biomimetic compound-eye imaging system. The multi-aperture optical flow field is first obtained from compound-eye through multi-scale analysis and Bayes threshold processing. The real distance to the ground is then obtained by extracting the disparity information of nine apertures for adaptive depth estimation. The velocity measurement error is less than 0.6m/s in low-altitude (8 m and 13 m) flight scenarios with multiple obstacles.

We developed an intelligent innovative orientation method to improve the accuracy of polarization compasses in harsh conditions: weak skylight polarization patterns resulting from unfavorable weather conditions (e.g., haze, sandstorms) or locally destroyed skylight polarization conditions caused by occlusions (e.g., buildings, trees). First, the skylight polarization status was determined with the degree of linear polarization threshold analysis method and a bionic polarization enhancement sensing model was constructed to simulate the enhanced perception mechanism identified in the Syrphidae visual neural pathway, highly efficient in dark or weakly illuminated environments. The bionic model successfully enhanced the information content extracted from weak polarization patterns. Second, polarization pixel interferences, caused by occlusions under locally destroyed skylight polarization conditions, were removed with a convolutional neural network for image segmentation and the sky area of interest was identified. Finally, the incomplete angle of polarization map derived after image segmentation was fitted using our optimized adaptive antisymmetric ring algorithm. On the basis of the strong angle-of-polarization antisymmetry along the solar meridian, information extracted from the sparse and irregular polarization pixels was analyzed to derive a high-accuracy polarization orientation solution. The whole method intelligently realizes pattern analysis and deep learning intelligent processing, efficiently rotates to manage polarization disorientation. The experimental results demonstrated the performance of the proposed method in compensating for reduced orientation accuracy under degraded polarization conditions, its robustness against perturbations, and its beneficial impact on the environmental adaptability of bionic polarization compasses.

Drone audio detection methods have become a key component of anti-drone systems. Traditional audio feature extraction methods have problems such as large fluctuations in feature vectors, fixed extraction resolution, and redundant feature information extraction. Moreover, the dataset is ideal and not representative of real application scenarios. In this study, a dual-domain audio feature extraction method in the time and frequency domains is proposed that improves the accuracy of drone detection by combining the more richly detailed information in the time domain and the relatively stable property of the signal in the frequency domain. A real-world sound dataset that contains low signal-to-noise ratio audio was collected for experimental validation. The results showed that, compared with existing methods, the proposed method took full advantage of the “zoom” feature of the wavelet packet transform, the local feature extraction capability of a one-dimensional convolutional neural network, and the global modeling capability of a self-attention mechanism, thereby effectively improving the success rate of drone detection in common scenarios. The proposed method also outperformed other methods with respect to several evaluation metrics.

In order to remove noise generated during the accelerometer calibration process, an accelerometer denoising method based on empirical mode decomposition (EMD) and time-frequency peak filtering (TFPF) is proposed in this paper. Firstly, a new design of the accelerometer structure is introduced and analyzed by finite element analysis software. Then, an algorithm combining EMD and TFPF is proposed for the first time to deal with the noise of the accelerometer calibration process. Specific steps taken are to remove the intrinsic mode function (IMF) component of the high frequency band after the EMD decomposition, and then to use the TFPF algorithm to process the IMF component of the medium frequency band; meanwhile, the IMF component of the low frequency band is reserved, and finally the signal is reconstructed. The reconstruction results show that the algorithm can effectively suppress the random noise generated during the calibration process. The results of spectrum analysis show that EMD + TFPF can effectively protect the characteristics of the original signal and that the error can be controlled within 0.5%. Finally, Allan variance is used to analyze the results of the three methods to verify the filtering effect. The results show that the filtering effect of EMD + TFPF is the most obvious, being 97.4% higher than the original data.

How to obtain reliable vehicle heading information in the event of GNSS rejection is the key part of the current navigation scheme. In this paper, a vehicle-mounted polarization orientation strategy for sloping roads and occlusion is investigated. For inclined road circumstances, a polarization orientation scheme with tilt compensation ability is designed which is not limited to the field of view of the image polarization compass, and a vision modulation model is provided. On this premise, a solar azimuth region orientation (SAO) strategy is developed, which lowers the measurement error caused by occlusion, and improves the image polarization compass's robustness under complex road conditions. Furthermore, outdoor experiments are delivered to testify the effectiveness of the method. In the vehicle experiment, the root mean square error (RMSE) is reduced by more than 82% when compared to the typical polarization orientation technique.