Abstract
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.
Highlights
Studies of certain insects and migratory birds provide rich evidence that they extract navigation cues from regular atmosphere polarization pattern [1]–[3], which gives stable and crucial reference information for bionic polarizationThe associate editor coordinating the review of this manuscript and approving it for publication was Jinming Wen .navigation in complex natural environment [4], [5]
The polarization compass is a key component in a polarized light navigation system, which is an autonomous navigation device depending on the atmosphere polarization mode of the polarized light in sky
Some research on improving the orientation performance of a polarization compass mainly focuses on the point of view optimizing the algorithm of heading angles, such as an orientation method via Pulse Coupled Neural Network algorithm is addressed for the highly accurate and robust compass information calculation from the polarized skylight imaging [6], a calculation algorithm of heading angles for an imaging polarization navigation sensor based on a machine-vision algorithm is proposed [7], a computational model can directly estimate the solar azimuth and infer the confidence of estimating the potential accuracy of polarized light compass information both in absolute terms and in the context of path integration [8]
Summary
Studies of certain insects and migratory birds provide rich evidence that they extract navigation cues from regular atmosphere polarization pattern [1]–[3], which gives stable and crucial reference information for bionic polarization. From the previous analysis, it can be seen the existing methods of improving the orientation accuracy for a polarization compass are through optimizing the algorithm of heading angles and integrated navigation. Neural network model such as backpropagation (BP) neural network, Elman neural network (Elman NN), and radical basis function (RBF) neural network have been employed to model nonlinear relationship between various data due to their flexible function approximators They perform poor abilities in improving the orientation accuracy for a polarization compass. In order to improve the heading accuracy effectively for a polarization compass, a novel denoising scheme using IVST-SIT is designed to eliminate the noise in the AoP images directly by integrating the strength of iterative variance-stabilizing transformation (IVST) [22], [23] and adaptive soft interval thresholding (SIT) [24].
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