Accurate and reliable navigation data is a key component in internet of things (IoT). High-precision and stable autonomous orientation has attracted considerable attention regarding environments in which the global navigation satellite system signal is unreliable. This study proposed a robust orientation method based on the atmospheric polarization mode called weather weighting sparse coding that includes weather classification, sparse coding and fitting. In comparison with previous studies, the proposed method is effective in restoring the angle of polarization image and achieving accurate orientation under complex weather. Specifically, to achieve high-precision orientation in complex weather, a polarization compass was designed, which used the characteristics of the different levels of destruction of atmospheric polarization images under different weather conditions for classification and denoising. The proposed strategy was used to process atmospheric polarization images obtained in complex weather. Experimental results showed that the orientation accuracy was better than 0.31∘ (Root Mean Square Error) under conditions of overcast, sandstorms, clear with overexposed areas, and smog with tree-obscured and overexposed areas.

Bionic polarization navigation has a broad variety of application in diverse fields for high reliability and strong robustness to interference, fundamental to which is the use of a polarization compass based on polarized light cues. Nevertheless, dramatical reduction of the orientation accuracy resulted from the noise in a measured angle of polarization (AoP) and the tilted angles of a polarization compass during operation gives imperative influence on navigation precision. Herein, we investigate how to improve the navigation accuracy effectively by the proposed comprehensive heading error processing technique for a polarization compass, where a novel denoising scheme is designed to eliminate the noise in AoP images directly by integrating the strength of iterative variance-stabilizing transformation (IVST) and adaptive soft interval thresholding (SIT) so as to compensate the following tilt-induced error accurately. Subsequently, a promising compensation approach inspired by efficient extreme learning machine (EELM) is introduced to correct the tilt-induced error caused by realistic execution. The AoP image denoising advance and the tilt-induced error modeling advance combine to produce remarkable performance gains on the heading error. Experimental results and comparisons with prior arts reveal that the proposed comprehensive heading error processing technique is highly appealing in terms of improving the orientation accuracy for a polarization compass with superiority to state-of-the-art alternatives.

Aiming at enhancing the accuracy and reliability of velocity calculation in vision navigation, an improved method is proposed in this paper. The method integrates Mask-R-CNN (Mask Region-based Convolutional Neural Network) and K-Means with the pyramid Lucas Kanade algorithm in order to reduce the harmful effect of moving objects on velocity calculation. Firstly, Mask-R-CNN is used to recognize the objects which have motions relative to the ground and covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then, the pyramid Lucas Kanade algorithm is used to calculate the optical flow value. Finally, the value is clustered by the K-Means algorithm to abandon the outliers, and vehicle velocity is calculated by the processed optical flow. The prominent advantages of the proposed algorithm are (i) decreasing the bad impacts to velocity calculation, due to the objects which have relative motions; (ii) obtaining the correct optical flow sets and velocity calculation outputs with less fluctuation; and (iii) the applicability enhancement of the optical flow algorithm in complex navigation environment. The proposed algorithm is tested by actual experiments. Results with superior precision and reliability show the feasibility and effectiveness of the proposed method for vehicle velocity calculation in vision navigation system.

In recent years, the polarization compass (PC) has been extensively applied in the field of autonomous navigation owing to its strong autonomy and anti-interference characteristics. Nevertheless, the existing research shows that the polarization compass with high extinction ratio polarization sensor still offers poor orientation accuracy since there is a great quantity of noise in the heading data output from the compass. Herein, we present a novel and promising scheme to de-noise heading data to improve deficiency in heading data de-noising of existing research. The purpose of this study aims to solve the problem that the orientation accuracy of PC caused by noise needs to be improved. Concretely, major points of this research include an effective separation strategy of noise and useful signal based on the optimized variational mode decomposition (OVMD), and an effective heading data de-noising method on the basis of an adaptive singular spectrum analysis (AD-SSA). OVMD based on the center frequency is employed to decompose the original noisy heading data into a finite number of band-limited data intrinsic mode functions (DIMFs) so that the best number of decomposed layers for the noising data can be obtained. Subsequently, the above decomposed DIMFs are divided into low frequency signal components, mixed components, and high frequency noise components exploiting the maximum similar eigenvalue of the autocorrelation matrix corresponding to each DIMF as a classification method. An adaptive singular spectrum analysis (AD-SSA) using the correlation coefficient to further suppress the noise in classified DIMFs. These approaches are combined effectively to improve the orientation accuracy of the polarization compass significantly and very contribute to the navigation application of unmanned mobile platforms such as unmanned aerial vehicles (UAV). Compared to existing prior arts, the effectiveness of the proposed heading de-noising scheme for the polarization compass is demonstrated through the results obtained from the heading data sets on two representative sunny and dust days.

Considering the error accumulation problem of a pure inertial navigation system (INS) under the condition of satellite signal outages, this paper proposes a brain-like navigation method based on a navigation cell model of the hippocampus to improve the accuracy and intelligence of the INS. The proposed method employs vision to acquire external perception information as an absolute reference for INS position error correction, which is established by analyzing the navigation mechanism of the rat brain. The prominent advantages of the presented method include: (1) a remarkable effect in reducing the accumulated errors of INS can be obtained; and (2) a hardware implementation procedure of an INS/vision brain-like navigation system on a single-chip microcomputer is designed and established, which makes possible the engineering application of the brain-like navigation system by providing technical detail. Also, an outdoor vehicle test is carried out to verify the superiority of the proposed INS/vision brain-like navigation system in position measurement. Finally, the optimal performance shows the effectiveness of the proposed method in accumulated error correction and accuracy improvement for INS.

The quality of polarization images is easy to be affected by the noise in the image acquired by a polarization camera. Consequently, a de-noising method optimized with a Pulse Coupled Neural Network (PCNN) for polarization images is proposed for a Field-Programmable Gate Array (FPGA)-based polarization camera in this paper, in which the polarization image de-noising is implemented using an adaptive PCNN improved by Gray Wolf Optimization (GWO) and Bi-Dimensional Empirical Mode Decomposition (BEMD). Unlike other artificial neural networks, PCNN does not need to be trained, but the parameters of PCNN such as the exponential decay time constant, the synaptic junction strength factor, and the inherent voltage constant play a critical influence on its de-noising performance. GWO is able to start optimization by generating a set of random solutions as the first population and saves the optimized solutions of PCNN. In addition, BEMD can decompose a complicated image into different Bi-Dimensional Intrinsic Mode Functions with local stabilized characteristics according to the input source image, and the decomposition result is able to lower the complexity of heavy noise image analysis. Moreover, the circuit in the polarization camera is accomplished by FPGA so as to obtain the polarization image with higher quality synchronously. These two schemes are combined to attenuate different types of noises and improve the quality of the polarization image significantly. Compared with the state-of-the-art image de-noising algorithms, the noise in the polarization image is suppressed effectively by the proposed optimized image de-noising method according to the indices of peak signal-to-noise ratio, standard deviation, mutual information, structural similarity, and root mean square error.