基于因子图的状态估计方法运算实时性研究CSCD

对因子图算法及其理论基础进行研究,使用消元算法分析了一元观测因子图模型的稀疏结构,得到了一种适用于该模型的增量推断算法;并对固定滞后平滑算法进行了研究,实现了滑窗在该增量推断算法中的应用;最后通过仿真验证了基于因子图的状态估计方法的估计能力、该增量推断算法在求解一元观测因子图模型的状态估计问题时运算速度的提升能力,以及滑窗维持该增量推断算法运算速度稳定的能力。     

Model based Computerized Ionospheric Tomography in space and time

Reconstruction of the ionospheric electron density distribution in space and time not only provide basis for better understanding the physical nature of the ionosphere, but also provide improvements in various applications including HF communication. Recently developed IONOLAB-CIT technique provides physically admissible 3D model of the ionosphere by using both Slant Total Electron Content (STEC) measurements obtained from a GPS satellite - receiver network and IRI-Plas model. IONOLAB-CIT technique optimizes IRI-Plas model parameters in the region of interest such that the synthetic STEC computations obtained from the IRI-Plas model are in accordance with the actual STEC measurements. In this work, the IONOLAB-CIT technique is extended to provide reconstructions both in space and time. This extension exploits the temporal continuity of the ionosphere to provide more reliable reconstructions with a reduced computational load. The proposed 4D-IONOLAB-CIT technique is validated on real measurement data obtained from TNPGN-Active GPS receiver network in Turkey.     

Remaining useful life prediction of aircraft lithium-ion batteries based on F-distribution particle filter and kernel smoothing algorithm

As an emergency and auxiliary power source for aircraft, lithium (Li)-ion batteries are important components of aerospace power systems. The Remaining Useful Life (RUL) prediction of Li-ion batteries is a key technology to ensure the reliable operation of aviation power systems. Particle Filter (PF) is an effective method to predict the RUL of Li-ion batteries because of its uncertainty representation and management ability. However, there are problems that particle weights cannot be updated in the prediction stage and particles degradation. To settle these issues, an innovative technique of F-distribution PF and Kernel Smoothing (FPFKS) algorithm is proposed. In the prediction stage, the weights of the particles are dynamically updated by the F kernel instead of being fixed all the time. Meanwhile, a first-order independent Markov capacity degradation model is established. Moreover, the kernel smoothing algorithm is integrated into PF, so that the variance of the parameters of capacity degradation model keeps invariant. Experiments based on NASA battery data sets show that FPFKS can be excellently applied to RUL prediction of Li-ion batteries.     

A new SINS/GPS sensor fusion scheme for UAV localization problem using nonlinear SVSF with covariance derivation and an adaptive boundary layer

The state estimation strategy using the smooth variable structure filter(SVSF) is based on the variable structure and sliding mode concepts. As presented in its standard form with a fixed boundary layer limit, the value of the boundary layer width is not precisely known at each step and may be selected based on a priori knowledge. The boundary layer width reflects the level of uncertainty in the model parameters and disturbance characteristics, where large values of the boundary layer width lead to robustness without optimality and small values of the boundary layer width provide optimality with poor robustness. As a solution and to overcome these limitations, an adaptive smoothing boundary layer is required to achieve greater robustness and suitable accuracy.This adapted value of the boundary layer width is obtained by minimizing the trace of the a posteriori covariance matrix. In this paper, the proposed new approach will be considered as another alternative to the extended Kalman filters(EKF), nonlinear H1 and standard SVSF-based data fusion techniques for the autonomous airborne navigation and self-localization problem. This alternative is based on strapdown inertial navigation system(SINS) and GPS data using the nonlinear SVSF with a covariance derivation and adaptive boundary layer width.Furthermore, the full mathematical model of the SINS/GPS navigation system considering the unmanned aerial vehicle(UAV) position, velocity and Euler angle as well as gyro and accelerometer biases will be used in this paper to estimate the airborne position and velocity with better accuracy.     

GNSS最优载波相位平滑伪距研究

平滑常数是影响载波相位平滑伪距精度的关键参数,实际数据处理时主要依据经验设定平滑常数。这种主观设定过程缺乏理论依据,无法达到最优平滑效果。针对此问题,以适用于实时GNSS载波相位平滑伪距的经典Hatch递推滤波算法为基础,在连续时间域上分析了载波相位平滑伪距误差的主要构成,给出了总误差估算公式,分析了平滑常数对平滑精度的影响传导机制。进一步,采用令平滑总误差最小为目标的极值法推导给出了最优载波相位平滑常数的计算公式,给出了最优载波相位平滑伪距的完整处理步骤。最优平滑常数算法在数学意义上最优,大幅压缩了伪距测量误差,又不会引入过大的电离层发散误差。通过两个实际算例,证明了算法有效性。     

用于提高整体叶盘叶片表面质量的数控工艺

针对五坐标加工中心加工整体叶盘时叶片表面质量较差的问题,本文提出了一套利用UG软件自身功能光顺曲线和曲面的方法,使造型曲面的光顺程度得到大幅度提高,从整体叶盘数控加工工艺源头上保证了整体叶盘加工质量.实践表明,此方法方便、快捷,叶片表面质量得到明显改善,可满足工程使用要求.     

用动网格方法模拟导弹发射过程中的燃气射流流场

采用局部非结构网格弹性变形方法和网格再生方法相结合的动网格技术，计算导弹与发射简具有相对运动的燃气射流非定常流场。计算中将安置在发射简内的导弹作为运动实体，随着导弹运动，相应流场计算域边界发生变化。计算结果表明，采用动网格技术模拟导弹发射过程中燃气射流的非定常流场，具有较高的精度。     

移动平滑算法在无人机遥测数据处理中的应用

研究了用于无人机飞行试验数据事后处理的平滑滤波及预报问题.提出白噪声多项式的最优线性移动平滑滤波算法应用于分离飞行遥测数据中的真实信号和随机噪声,并通过滤波方差比给出对随机误差的压缩程度.试验结果表明,该方法对各种飞行数据有效、可靠、适用.     

基于改进TLS-ESPRIT算法的相干定位信号波达方向空间平滑

针对室内角度定位,在定位信号相干条件下无法实现准确估计波达方向(DOA)的问题,提出了一种基于TLS-ESPRIT算法的双向空间平滑方法。该算法先对定位相干信号进行双向空间平滑处理,然后用TLS-ESPRIT算法进行DOA的精确估计,使其最大限度地利用信号子空间的信息,从而解决了算法计算过程中输出信号的协方差矩阵秩亏损情况。该算法能够准确实现定位信号相干条件下DOA估计,与传统的空间平滑结合其他改进ESPRIT算法相比,具有更好的平稳性及抗干扰能力。最后通过仿真实验验证了算法的有效性和稳定性。     

无人机定向天线自跟踪系统研究

为了满足无人机(UAV)远距离通信的需要,设计研制了以无人机位置信息为引导的地面定向天线自跟踪系统。基于实时获取定向天线载车位置、速度、方位和俯仰角等信息,实现了定向天线在运动中对目标进行自动跟踪的能力,提高了系统的机动性和隐蔽性。针对目前利用2点位置来计算目标角的方法存在需要通过相对位置才能确定实际目标角的不足,提出了一种不需要考虑2点相对位置就能直接计算出目标角的计算方法,为工程应用带来了方便。针对定向天线地面载车在机动时GPS信号容易受干扰,从而造成跟踪目标丢失,以及因无人机位置信息更新频率较低而造成的跟踪系统运动抖动、跟踪误差较大等问题,提出了一种基于速度对位置进行预测平滑的控制策略,有效解决了以上问题,并显著提高了跟踪精度。设计研制了定向天线自跟踪系统的软硬件,并进行了测试,结果表明:研制的定向天线具有机动跟踪能力和较高的跟踪精度,能满足无人机对定向天线自跟踪的需要。