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.

An adaptive multi-scale method based on the combination generalized morphological filter (CGMF) is presented for de-noising of the output signal from a MEMS gyroscope. A variational mode decomposition is employed to decompose the original signal into multi-scale modes. After choosing a length selection for the structure element (SE), the adaptive multi-scale CGMF method reduces the noise corresponding to the different modes, after which a reconstruction of the de-noised signal is obtained. From an analysis of the effect of de-noising, the main advantages of the present method are that it: (i) effectively overcomes deficiencies arising from data deviation compared with conventional morphological filters (MFs); (ii) effectively targets the different components of noise and provides efficacy in de-noising, not only primarily eliminating noise but also smoothing the waveform; and (iii) solves the problem of SE-length selection for a MF and produces feasible formulae of indicators such as the power spectral entropy and root mean square error for mode evaluations. Compared with the other current signal processing methods, the method proposed owns a simpler construction with a reasonable complexity, and it can offer better noise suppression effect. Experiments demonstrate the applicability and feasibility of the de-noising algorithm.

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.

Aimed at the problem of poor noise reduction effect and parameter uncertainty of pulse-coupled neural network (PCNN), a hybrid image denoising method, using an adaptive PCNN that has been optimized by grey wolf optimization (GWO) and bidimensional empirical mode decomposition (BEMD), is presented. The BEMD is used to decompose the original image into multilayer image components. After a GWO is run to complete PCNN parameter optimization, an adaptive PCNN filter method is used to remediate the polluted noise points that correspond to the different image components, from which a reconstruction of the denoised image components can then be obtained. From an analysis of the image denoising results, the main advantages of the proposed method are as follows: (i) the method effectively solves the deficiencies that arise from the critical PCNN parameter determination issue; (ii) the method effectively overcomes the problem of high-intensity noise effects by providing a more targeted and efficient noise reduction process; (iii) when using this method, the noise points are isolated, and the original pixel points are restored well, which can lead to preservation of image detail information. When compared with traditional image denoising process algorithms, the proposed method can yield a better noise suppression effect, based on indicators including analysis of mutual information (MI), structural similarity (SSIM), the peak signal-to-noise ratio (PSNR) and the standard deviation (STD). The feasibility and applicability of the proposed denoising algorithm are also demonstrated experimentally.

A multiscale parallel processing model of heading error for the polarization compass (PC) is developed in this article. Existing state-of-the-art schemes of raising heading measurement precision are implemented relying on not only ideal assumption in noiseless heading output from the PC but also the polynomial method employed to calculate the heading error caused by the varied attitude angles of PC, which leads to fall sharply in the PC heading accuracy in real applications. Moreover, the aforementioned denoising and heading error compensation are conducted independently. Here we specifically concentrate on a novel and promising parallel processing model for heading error as a way to improve the PC heading accuracy, where a comprehensive analysis of heading error sources is first carried out for the PC. Subsequently, the proposed multiscale parallel error processing model is presented to improve heading accuracy for the PC by repressing noise as well as compensating heading errors that are implemented synchronously. Specifically, variational mode decomposition (VMD) is exploited to decompose the original heading data output from PC into multiple intrinsic mode functions, which characterizes binned modes (BIMFs) with high-to-low frequencies by seeking the ensemble of the decomposed modes and their respective center frequencies. An adaptive multiscale principle component analysis (MS-PCA) scheme denoising approach exploiting the principle contribution rate is performed to attenuate the noise in the high-frequency BIMFs. An effective compensation approach with a long short-term memory (LSTM) modeling is introduced straightforward to compensate for the heading error for the low-frequency BIMF concurrently. The rotational test is carried out to verify that the heading error processed by VMD-PCA denoising and LSTM compensating, which are the most effective among the comparison algorithms and the RMSE of 0.3505 and 0.2668, increased by 80.10% and 82.00%, respectively. The UAV flight test demonstrates that the RMSE of the parallel scheme using VMD-PCA and LSTM performed simultaneously is the minimum values of 0.1282 and 71% higher than the original navigation error. It can be seen from various experimental results that the proposed multiscale parallel processing model of denoising and error compensation procedure performed synchronously shows attractive performance gains with respect to emerging single processing in terms of increasing the accuracy of the PC heading measurement.