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.

Chapter 1 Introduction
1.1 Development Background and Research Significance
1.2 Bioinspired polarization orientation method
1.3 Orientation error processing method for bioinspired polarization compass
1.4 Combined orientation system and method for bioinspired polarizaition compass/inertial navigation
Chapter 2 Orientation Method and System for Atmospheric Polarization Pattern
2.1 Orientation method for atmospheric polarization pattern
2.1.1 Analysis and automatic identification of neutral point characteristics of atmospheric polarization pattern
2.1.2 Orientation algorithm based on solar meridian for imaging bioinspired polarization compass
2.2 Design and integration for bioinspired polarization compass based on FPGA
2.3 Verification of Bioinspired Polarization compass orientation test
2.3.1 Static orientation test
2.3.2 Turntable dynamic orientation test
2.3.3 UAV airborne dynamic orientation test
2.4 Chapter Summary
Chapter 3 Processing technology for Bioinspired polarization compass noise
3.1 Noise analysis for bioinspired polarization compass
3.1.1 Analysis of the generation mechanism and characteristics for polarization angle image noise
3.1.2 Analysis of the generation mechanism and characteristics for heading angle data noise
3.2 Image denoising technology based on multi-scale transformation for bioinspired Polarization compass
3.2.1 Denoising technology for polarization angle image based on multi-scale transformation
3.2.2 MS-PCA Image Denoising Technology based on BEMD for Bioinspired Polarization Compass
3.2.3 Verification of MS-PCA polarization angle image denoising method based on BEMD
3.3 Heading data denoising technology based on multi-scale transformation for bioinspired polarization compass
3.3.1 Heading data denoising technology based on multi-scale transformation
3.3.2 MS-TFPF heading data denoising technology based on EEMD for bioinspired polarization compass
3.4 Verification of heading data denoising based on multi-scale transformation for bioinspired polarization compass
3.5 Chapter Summary
Chapter 4 Orientation error modeling and compensation technology for Bioinspired polarization compass
4.1 Polarization orientation error analysis and model
4.1.1 Analysis of polarization orientation error
4.1.2 Model Construction for polarization orientation error
4.2 Typical neural network models
4.2.1 Recurrent Neural Networks (RNNs)
4.2.2 Long Short-Term Memory Neural Networks (LSTMs)
4.2.3 Gated Recurrent Unit Neural Networks (GRUs)
4.3 Modeling and compensation of orientation error based on GRU deep learning neural network for bioinspired polarization compass
4.4 Experimental verification of orientation error model based on GRU deep learning neural network for bioinspired polarization compass
4.5 Chapter summary
Chapter 5 Seamless combined orientation method and system for bioinspired polarization compass/inertial navigation
5.1 Seamless combined orientation system for bioinspired polarization compass/inertial navigation
5.2 Seamless combination orientation model construction for bioinspired polarization compass/inertial navigation
5.3 Seamless combined orientation method based on self-learning multi-frequency residual correction for bioinspired polarization compass/inertialnavigation
5.4 Experimental verification of the seamless combined orientation method for bioinspired polarization compass/inertial navigation
5.5 Chapter summary
Chapter 6 Summary and prospect
6.1 Summary of intelligent information processing technology for bioinspired polarization compass
6.2 Research outlook
References