惯性基偏振罗盘定向信息处理技术（INFORMATION PROCESSING TECHNOLOGY FOR BIOINS

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