DQ-1宽幅成像光谱仪发射前载荷定标技术

大气环境监测卫星作为国家民用空间基础设施规划中的科研卫星,其遥感应用需求对其载荷的定量化精度要求越来越重要。宽幅成像光谱仪作为大气环境监测卫星中的主要载荷,可获取光谱范围从可见光至长波红外(0.415~12.000 μm)的陆表和大气多光谱信息。采用45°扫描镜配合消旋系统的扫描方式,光路结构采用同轴望远镜系统,实现3个探测器焦面上21个谱段的同时对地的超宽幅高空间分辨率成像。为了准确地获取能量和仪器响应之间的定量关系,在卫星发射前开展宽幅成像光谱仪全光路、全口径辐射定标试验,分别介绍了可见到短波谱段积分球定标技术和中长波谱段热真空红外定标技术,为用户定量应用提供了良好的保障,并对定标过程中传递路径下的误差来源及精度进行分析评估。     

阵元指向未知条件下的信号波达方向估计

提出了一种适用于阵元指向未知情形的极化敏感阵列信号波达方向(DOA)估计方法。该方法利用降秩准则将信号方向、极化和阵元指向信息进行解耦,通过唯角度参数搜索完成信号DOA估计,避免了复杂的多维寻优。讨论了新方法实现无模糊DOA估计的必要条件,并将其与利用错误指向信息的DOA估计方法进行了性能比较。结果表明:对于阵元指向未知或存在一定误差的场合,该算法的估计性能更优。     

一种基于流量法的大型贮罐容积标定系统

以新一代运载火箭燃料贮罐容积标定系统项目研制为背景,介绍了一种基于流量法的大型贮罐容积标定系统的设计思路、原理、方法和组成,并对系统的测量不确定度进行了理论评定。该系统不仅可以得到比较准确的过程参数,且可以推广应用于各种大型容器的"液位—容积"或"容积—液位"的准确标定。     

统一测控系统跟踪在轨目标自动校相方法研究

以船载S波段统一测控系统为例,阐述通过引导天线跟踪加偏和数字引导跟踪加中心修正再加偏两种手段,对在轨运动目标跟踪前进行自动相位校准的方法。试验结果表明,方法可行,可以推广应用。     

动态发现网络拓扑关系在载人航天任务中的应用研究

介绍了网络拓扑关系动态发现的基本知识,通过对基于地址转发表的拓扑发现算法的分析,提出了基于STP状态表的拓扑发现算法,实现动态发现网络拓扑关系。该算法降低了算法实现的复杂性并提高了拓扑发现的有效性,在载人航天任务中发挥了作用。     

Ka频段大口径测控天线无人机校相方法设计与验证

面向Ka频段航天测控设备天线标校工程应用需求,分析了大口径测控天线相位校准方法原理,论证设计了轻量化、模块化的组合式测控应答机无人机装载模式,提出了基于无人机的S频段天线自跟踪叠加偏置进行Ka频段天线快速校相的方法,并进行了跟踪精度分析。计算了在不同地面测控天线口径下进行相位标校的链路余量,规划了无人机飞行航路和试验步骤,并进行了Ka频段天线快速校相、电轴一致性测试试验,每次相位标校得出的交叉耦合和灵敏度系数指标均满足天线对无人机信标的闭环自跟踪要求。结果表明,基于无人机平台的大口径测控天线相位校准方法能够为Ka频段测控设备标校提供一种新的手段,具有良好的工程应用前景。     

高空台特种调节阀建模方法对比研究

为了获得准确的轮盘式特种调节阀流量特性模型,提高高空舱进口流量预测精度,提出了基于BP神经网络和NARX网络的建模方法。在对调节阀与传感器测点位置分析的基础上,将调节阀和阀后容腔作为整体进行建模。对比研究了流量系数、静态BP神经网络以及基于Gamma Test的动态NARX网络建模方法,并给出了工程中选取建模方法的建议。以试验流量数据为基准,仿真对比了不同阀门开度变化时,各模型输出流量的稳态误差和动态误差。结果表明,BP神经网络方法和NARX网络方法建模精度要优于流量系数法。同时,BP神经网络模型最大稳态误差为0.52kg/s,优于NARX网络模型和流量系数模型。NARX网络模型的最大动态误差为2.04kg/s,相比于BP神经网络模型和流量系数模型,能够更准确地反映流量的动态特性。     

Positioning error compensation for parallel mechanism with two kinematic calibration methods

Compared with serial mechanisms, the parallel mechanism (PM) theoretically exhibited higher positioning accuracy, dynamic performance, strength-to-weight ratio, and lower manufacturing cost, but they had not been widely used in the practical application. One key issue, positioning accuracy, which directly affected their performance and was greatly influenced by the errors of kinematic structure parameters was analyzed. To effectively enhance the positioning precision of PMs, a novel modeless kinematic calibration method, namely the split calibration, was presented and its compensation effect of the positioning error was comprehensively compared with that of an integrated method on two different types of PMs. A strange phenomenon-correct and incorrect identified results were derived from two different PMs by the same integrated method, respectively-which had not been reported yet was discovered, and the origin of it was revealed utilizing numerical simulations. Finally, respective merits and drawbacks of these two methods obtained in this paper provided underlying insights to guide the practical application of the kinematic calibration for PMs.     

Lightweight hybrid visual-inertial odometry with closed-form zero velocity update

Visual-Inertial Odometry (VIO) fuses measurements from camera and Inertial Measurement Unit (IMU) to achieve accumulative performance that is better than using individual sensors. Hybrid VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter (MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, one-dimensional inverse depth parametrization is utilized to parametrize the augmented feature state. This modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene, a novel closed-form Zero velocity UPdaTe (ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.^①     

空间机器人动力学奇异回避的笛卡尔轨迹规划

针对空间机器人动力学奇异的回避问题，提出一种基于组合函数的笛卡尔轨迹规划方法。将梯形规划与正弦函数相结合，对机械臂末端的位姿进行参数化。机械臂沿着某一轨迹运动时，根据阻尼最小方差法(DLS)的特点，提出一种判断是否发生动力学奇异的方法，并据此进行轨迹规划。该方法可以保证空间机械臂运动过程中不会遇到动力学奇异。此外，将基座姿态扰动和机械臂运动时间作为目标函数的一部分，最终，轨迹规划问题转化为多目标优化问题，并利用混合整数规划的混沌粒子群优化算法(CPSO)进行求解。该优化算法能够改善标准粒子群算法(PSO)的“早熟”现象。仿真结果表明，新方法能够有效处理动力学奇异问题，减小基座姿态扰动及运动时间。