Instantaneous BeiDou–GPS attitude determination: A performance analysis

The advent of modernized and new global navigation satellite systems (GNSS) has enhanced the availability of satellite based positioning, navigation, and timing (PNT) solutions. Specifically, it increases redundancy and yields operational back-up or independence in case of failure or unavailability of one system. Among existing GNSS, the Chinese BeiDou system (BDS) is being developed and will consist of geostationary (GEO) satellites, inclined geosynchronous orbit (IGSO) satellites, and medium-Earth-orbit (MEO) satellites. In this contribution, a BeiDou–GPS robustness analysis is carried out for instantaneous, unaided attitude determination.     

利用高分辨率遥感数据的芦山地震道路损毁快速评估

道路损毁评估是地震损失评估工作的重要内容。文章根据芦山7.0级地震应急评估需求,提出利用多源遥感数据的道路损毁评估方法。首先,针对获取的高分辨率遥感数据特点,建立道路损毁遥感解译标准,进一步考虑地震平均烈度,建立以县为单元的灾区道路损毁等级快速评估模型,最后给出了地震灾区基于高分辨率遥感数据的快速制图产品和灾区道路损毁分县等级评估结果。结果表明,道路损毁极重灾区有芦山县、宝兴县,重灾区有天全县、雨城区、荥经县、名山区、邛崃市,一般灾区有汉源县等14个县(市、区)。相关研究和结果为震后救灾决策提供了重要支撑。     

传感器故障下的航天器状态监测与故障识别

针对航天器上传感器故障可能导致状态估计错误的问题,提出了一种基于证据理论的多传感器融合与故障识别方法。该方法采用证据距离与冲突质量的乘积来度量证据间的冲突大小。在证据的融合中先根据冲突大小计算每个证据的可信度,并根据可信度大小修正证据,再用D\|S规则进行融合。该冲突度量方法综合了证据间目标一致性与整体差异性的影响,在具有较强的不确定性和不可靠性的情况下,比仅采用证据距离或冲突质量来度量证据间冲突的大小更加准确。将其应用于微小卫星电源系统的状态监测,实验结果表明该方法可以有效地监测系统状态并正确地识别故障传感器,准确度优于其它方法。     

基于空域稀疏性的宽带DOA估计

利用宽带阵列接收信号的空域稀疏性,将宽带信号的波达方向(DOA)估计转化为一个稀疏信号重构的问题,提出了一种新的宽带信号DOA估计算法。该算法将宽带信号分解为多个子带信号,联合利用多个子带信号的空域稀疏性进行重构。它是对用于稀疏重构的标准的稀疏贝叶斯学习算法的推广,可适用于多冗余字典的信号模型。另外,通过对多快拍的阵列接收信号进行奇异值分解(SVD),提取信号子空间作为算法的输入数据,可以在有效减少运算复杂度的同时,提高对噪声的稳健性。与传统的宽带阵列DOA估计方法相比,该算法能够用于低信噪比、快拍有限和信源相关性较高的场合,同时算法的性能对信源个数的估计值不太敏感。仿真实验表明,该算法相对现有的基于子空间类的方法,具有更好的DOA估计性能。     

利用脉组间频率步进信号合成UWB距离像及类MUSIC法对速度的高分辨估计(英文)

提出一种称为类多信号分类(CMUSIC)的方法,其利用脉组间频率步进信号,在具有M个脉冲的脉冲串(子带,即窄带)中高分辨估计径向速度。利用此速度估计值补偿距离-多普勒耦合和多普勒色散后,对跨子带进行快速傅里叶变换则可获得合成超宽带高分辨距离像。和其他方法相比,CMUSIC在低信噪比时具有较好的速度估计性能。当M大于具有不同径向速度的目标个数Q时,该方法在小M值下依然有优越的速度估计性能。此外,经过径向速度解模糊后,该方法适应高径向速度运动的目标。随着国防技术的突飞猛进地发展以及高速先进飞行器的涌现,这具有重要的实际价值。仿真结果验证了该方法的可行及有效性。     

GNSS多频观测对定姿精度的影响分析

针对单历元定姿的精度问题,对多频双差观测模型下的定姿精度进行了理论推导,分析了影响定姿精度的各种因素,并着重研究了GPS和Galileo系统下多频观测值对定姿精度的改善程度。理论分析和仿真实验均表明．相比于单频定姿,多频观测可以显著提高定姿精度。     

一种空间邻近目标红外多传感器立体分辨方法

空间邻近目标在红外像平面的成像因相互交叠而形成簇状像斑,对红外传感器的信号处理提出了分辨的要求。为实现对空间邻近目标的立体跟踪定位,提出基于量子粒子群优化的空间邻近目标红外多传感器立体分辨方法。在对目标像平面成像建模基础上,构建基于最小二乘准则的空间邻近目标立体分辨目标函数,针对目标函数的高维非线性特点,以QP-SO解决目标函数优化问题,估计目标空间位置。仿真结果表明:相比于传统的先单传感器像平面分辨后多传感器视线交叉定位方法,此法具有更优的目标位置估计精度、辐射强度估计精度和目标个数估计正确率。     

Aerial-BiSeNet: A real-time semantic segmentation network for high resolution aerial imagery

The aircraft system has recently gained its reputation as a reliable and efficient tool for sensing and parsing aerial scenes. However, accurate and fast semantic segmentation of high-resolution aerial images for remote sensing applications is still facing three challenges: the requirements for limited processing resources and low-latency operations based on aerial platforms, the balance between high accuracy and real-time efficiency for model performance, and the confusing objects with large intra-class variations and small inter-class differences in high-resolution aerial images. To address these issues, a lightweight and dual-path deep convolutional architecture, namely Aerial Bilateral Segmentation Network (Aerial-BiSeNet), is proposed to perform real-time segmentation on high-resolution aerial images with favorable accuracy. Specifically, inspired by the receptive field concept in human visual systems, Receptive Field Module (RFM) is proposed to encode rich multi-scale contextual information. Based on channel attention mechanism, two novel modules, called Feature Attention Module (FAM) and Channel Attention based Feature Fusion Module (CAFFM) respectively, are proposed to refine and combine features effectively to boost the model performance. Aerial-BiSeNet is evaluated on the Potsdam and Vaihingen datasets, where leading performance is reported compared with other state-of-the-art models, in terms of both accuracy and efficiency.     

宽带信号多通道干涉仪测向解模糊方法研究

针对基于子频带分解的宽带信号干涉法测向中通道间相位差的模糊问题,提出了一种简单的解模糊方法,该方法运算量小且正确解模糊对信噪比要求低。仿真结果表明了结论的正确性。