Polarized Compass Orientation Framework Based on Real-Time Discrimination of Heading Information in Harsh Weather

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Donghua Zhao;Nan Li;Bo Wang;Jinguang Yue;Qing Zhang;Chong Shen
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Abstract

How to achieve stable and reliable heading information in the case of harsh weather environments and global satellite navigation system (GNSS)-denied scenarios is more challenging for autonomous navigation. In recent years, the bioinspired polarized light orientation has been extensively and deeply explored in the field of autonomous navigation due to its high reliability and strong anti-interference performance. Herein, a novel polarized compass orientation framework in terms of real-time heading information discrimination is skillfully formulated and included in monitoring heading signal changes to characterize navigation behaviors without training complex neural networks or accumulating a large amount of data to drive. This study aims to solve the problem of the polarization compass orientation error caused by external environmental and internal device noise. Different from existing research, an optimized live heading signal discriminating and monitoring strategy based on sparse shrinkage local Fisher discriminant analysis (OSLFDA) is investigated in this article to distinguish the different data states comprised of pure heading signal and mixed noise without model traversal. Particularly, a discriminant function is calculated by formulating a heading data state discriminant matrix with the weight coefficient to maximize interclass and minimize intraclass separation. On this basis, an effective noise-suppressing algorithm based on a modified sliding singular spectrum analysis (MSSA), similarly without the need for a large amount of training neural networks, is presented to attenuate the noise in identified noisy components by OSLFDA so as to preserve the true heading signal to the maximum extent. Various representative experiment results demonstrate the efficacy and feasibility of the polarized compass orientation framework for drone navigation. In the drone-mounted test experiments on dust days, the root-mean-square error (RMSE, 0.5116°) is reduced by more than 81% when compared to the current up-to-date typical polarized light orientation techniques.
恶劣天气下基于航向信息实时判别的极化罗经定位框架
如何在恶劣天气环境和全球卫星导航系统(GNSS)拒绝的情况下获得稳定可靠的航向信息,是自主导航面临的更大挑战。近年来,仿生偏振光定向以其高可靠性和强抗干扰性在自主导航领域得到了广泛而深入的探索。本文巧妙地构建了一种基于实时航向信息判别的新型极化罗经定向框架,并将其纳入航向信号变化监测中,在不需要训练复杂神经网络或积累大量数据驱动的情况下表征导航行为。本研究旨在解决由外部环境和内部设备噪声引起的极化罗经定位误差问题。与已有研究不同,本文研究了一种基于稀疏收缩局部Fisher判别分析(OSLFDA)的优化实时航向信号判别与监测策略,以区分由纯航向信号和混合噪声组成的不同数据状态,而无需模型遍历。特别地,通过用权系数构造一个船首数据状态判别矩阵来计算判别函数,使类间分离最大化,类内分离最小化。在此基础上,提出了一种有效的基于改进滑动奇异谱分析(MSSA)的降噪算法,同样不需要大量的神经网络训练,通过OSLFDA对识别出的噪声分量进行降噪,最大程度地保留真实航向信号。多个有代表性的实验结果验证了极化罗经定位框架用于无人机导航的有效性和可行性。在沙尘天气的无人机测试实验中,与目前最新的典型偏振光定向技术相比,均方根误差(RMSE, 0.5116°)降低了81%以上。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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