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This book systematically and comprehensively elaborates on the intelligent information processing technology for a bioinspired polarization compass. The content of this book are briefly consisted of three parts. The research background and significance of intelligent information processing technology for a bioinspired polarization compass is introduced first, which analyzes the research status, development trends, and gap with foreign countries in the field of orientation methods based on atmospheric polarization pattern, as well as the processing methods of the orientation error for a bioinspired polarization compass and integrated system information processing. Subsequently, the noise components of a bioinspired polarization compass and the impact of noise on its directional accuracy is analyzed, introducing the denoising and orientation error compensation technique based on intelligent algorithms such as multi-scale principal component analysis and multi-scale adaptive time-frequency peak filtering. The third part focuses on the application of cubature Kalman filter and their improvement methods in seamless combination orientation systems based on a bioinspired polarization compass. A seamless combination orientation model under discontinuous observation conditions is proposed and a discontinuous observation algorithm based on neural networks is designed.

Chapter1  Introduction	1
1.1  Development Background and Research Significance	1
1.2  Bioinspired polarization orientation method	3
1.3  Orientation error processing method for bioinspired polarization 
compass	13
1.4  Combined orientation system and method for bioinspired polarizaition 
compass/inertial navigation	20
Chapter2  Orientation Method and System for Atmospheric Polarization 
Pattern	27
2.1  Orientation method for atmospheric polarization pattern	28
2.1.1  Analysis and automatic identification of neutral point characteristics of atmospheric polarization pattern	28
2.1.2  Orientation algorithm based on solar meridian for imaging 
bioinspired polarization compass	32
2.2  Design and integration for bioinspired polarization compass based on 
FPGA	37
2.3  Verification of Bioinspired Polarization compass orientation test	41
2.3.1  Static orientation test	46
2.3.2  Turntable dynamic orientation test	48
2.3.3  UAV airborne dynamic orientation test	49
2.4  Chapter Summary	53
Chapter3  Processing technology for Bioinspired polarization 
compass noise	55
3.1  Noise analysis for bioinspired polarization compass	56
3.1.1  Analysis of the generation mechanism and characteristics for 
polarization angle image noise	56
3.1.2  Analysis of the generation mechanism and characteristics for heading 
angle data noise	63
3.2  Image denoising technology based on multi-scale transformation for bioinspired Polarization compass	65
3.2.1  Denoising technology for polarization angle image based on 
multi-scale transformation	68
3.2.2  MS-PCA Image Denoising Technology based on BEMD for 
Bioinspired Polarization Compass	72
3.2.3  Verification of MS-PCA polarization angle image denoising method 
based on BEMD	77
3.3  Heading data denoising technology based on multi-scale transformation 
for bioinspired polarization compass	92
3.3.1  Heading data denoising technology based on multi-scale 
transformation	93
3.3.2  MS-TFPF heading data denoising technology based on EEMD for 
bioinspired polarization compass	96
3.4  Verification of heading data denoising based on multi-scale 
transformation for bioinspired polarization compass	104
3.5  Chapter Summary	115
Chapter4  Orientation error modeling and compensation technology for 
Bioinspired polarization compass	118
4.1  Polarization orientation error analysis and model	119
4.1.1  Analysis of polarization orientation error	119
4.1.2  Model Construction for polarization orientation error	125
4.2  Typical neural network models	128
4.2.1  Recurrent Neural Networks (RNNs)	128
4.2.2  Long Short-Term Memory Neural Networks (LSTMs)	133
4.2.3  Gated Recurrent Unit Neural Networks (GRUs)	141
4.3  Modeling and compensation of orientation error based on GRU deep 
learning neural network for bioinspired polarization compass	145
4.4  Experimental verification of orientation error model based on GRU 
deep learning neural network for bioinspired polarization compass	152
4.5  Chapter summary	156
Chapter5  Seamless combined orientation method and system for bioinspired 
polarization compass/inertial navigation	158
5.1  Seamless combined orientation system for bioinspired polarization compass/inertial navigation	160
5.2  Seamless combination orientation model construction for bioinspired 
polarization compass/inertial navigation	162


5.3  Seamless combined orientation method based on self-learning 
multi-frequency residual correction for bioinspired polarization 
compass/inertial navigation	166
5.4  Experimental verification of the seamless combined orientation method 
for bioinspired polarization compass/inertial navigation	176
5.5  Chapter summary	185
Chapter6  Summary and prospect	187
6.1  Summary of intelligent information processing technology for 
bioinspired polarization compass	187
6.2  Research outlook	190
References	192

Bioinspired polarized light navigation, as a new type of highly autonomous navigation manner, is based on the principle of insects using atmospheric polarization pattern for autonomous orientation, and possesses the advantages of strong autonomy, good concealment, large working range, and long working time. Not only the orientation error is not accumulated over time, but also the directional signal comes from nature and not affected by modern information warfare interference in this way. This method has significant guiding significance for solving the scientific problem of complete rejection of satellite navigation information together with the accumulation of errors over time when inertial navigation systems work alone, which has become an important direction for the development of navigation technology. Accordingly, it has been highly valued by domestic and foreign researchers and related research work has received strong support from governments, enterprises, and other levels at home and abroad. During the operation of a bioinspired polarization compass, there are errors in its output information due to the working principle and environment of the device itself, which includes the errors caused by polarization angle image noise, heading angle data noise, and changes in the attitude angle of a bioinspired polarization compass. In complex environmental conditions that encounters occlusion effects such as clouds, tunnels, and buildings, the bioinspired polarization compass becomes temporarily unavailable, which seriously reduces its directional accuracy and robustness. Therefore, it is extremely necessary to study the information processing technology for a bioinspired polarization compass. In the process of writing the book, the technical accumulation in the information processing of a bioinspired polarization compass over the years is summarized and is analyzed in detail. These methods mainly includes: analyzing and processing the noise in polarization angle images and heading angle data, modeling and compensating for the orientation errors, and a seamless combination orientation method based on a bioinspired polarization compass. A detailed introduction to heading angle measurement methods based on the atmospheric polarization pattern is also provided.
In the process of writing the book, I referred to the relevant materials of some brother universities and received guidance and help from many professors in the Tianjin University and North University of China. I would like to express my gratitude.
Owing to the limited level of the author and the shortcomings in the book, we kindly request criticism and correction from readers.

Donghua Zhao
December 2023

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惯性基偏振罗盘定向信息处理技术（Information Processing Technology for Bioinspired Polarization Compass）