Gaussian Mixture Filter-Incorporated Self-Attention Residual Neural Network for UAV Joint Error Identification and Target Localization

IF 5.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Xiuli Xin;Xinran Chen;Hongyu Zhou;Xiaoxue Feng;Weixing Li;Zhenxu Li;Feng Pan
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引用次数: 0

Abstract

Target localization is a key technology for unmanned aerial vehicle (UAV) applications in various fields, such as target tracking and task planning. However, the accuracy of UAV localization is significantly affected by systematic and random errors in attitude data, and the nonlinearity of the measurement model, together with the unknown distribution of measurement noise. To achieve robust and precise localization in long-distance oblique scenarios based on dynamic platforms, this article proposes a Gaussian Mixture Filter-incorporated self-attention (SA) Residual Neural Network (GMAR) algorithm for target localization. Firstly, an end-to-end SA residual neural network (SA-ResNN) model is built to accurately model both systematic and random errors in attitude angle. The SA mechanism is innovatively introduced to enhance the global feature representation capability of the residual module. Then, the Gaussian mixture (GM) filter utilizes a GM model to model the prior and posterior probability density functions, which can effectively capture the uncertainty in the state probability density function under nonlinear measurement models and enhance the robustness of the localization system. Finally, simulations and flight experiments demonstrate that the proposed GMAR algorithm can significantly improve the localization accuracy and robustness of ground targets in long-distance oblique scenarios.
基于高斯混合滤波的残差神经网络联合误差辨识与目标定位
目标定位是无人机在目标跟踪、任务规划等多个领域应用的关键技术。然而,姿态数据的系统误差和随机误差、测量模型的非线性以及测量噪声的未知分布会显著影响无人机定位的精度。为了实现基于动态平台的远距离倾斜场景下的鲁棒精确定位,本文提出了一种基于高斯混合滤波的自关注残差神经网络(GMAR)目标定位算法。首先,建立端到端SA残差神经网络(SA- resnn)模型,对姿态角的系统误差和随机误差进行精确建模;创新地引入SA机制,增强残差模块的全局特征表示能力。然后,高斯混合(GM)滤波器利用GM模型对先验和后验概率密度函数进行建模,可以有效捕获非线性测量模型下状态概率密度函数中的不确定性,增强定位系统的鲁棒性;仿真和飞行实验表明,该算法能显著提高远距离倾斜场景下地面目标的定位精度和鲁棒性。
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来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
期刊介绍: Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.
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