排序方式: 共有67条查询结果,搜索用时 484 毫秒
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针对仅含角度测量信息的单个天基平台可观测性较弱的问题,提出了一种含脉冲机动检测的空间非合作目标跟踪算法,并设计了非合作目标实时跟踪数据处理流程.该算法利用抗差估计技术和UKF(Unscented Kalman Filter,无迹卡尔曼滤波)算法构造目标跟踪滤波器,并综合残差多项式拟合和新息分布特征等方法实现目标机动检测,在天基平台观测信息类型有限和观测几何较差的情况下,可以同时排除孤立野值和成片测量野值的影响,实现非合作机动目标的连续稳定跟踪.数值实验验证了算法的可行性和有效性,也表明了跟踪精度和可靠性与测量精度密切相关. 相似文献
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Fault diagnosis based on measurement reconstruction of HPT exit pressure for turbofan engine 总被引:1,自引:0,他引:1
Aero-engine gas path health monitoring plays a critical role in Engine Health Management(EHM). To achieve unbiased estimation, traditional filtering methods have strict requirements on measurement parameters which sometimes cannot be measured in engineering. The most typical one is the High-Pressure Turbine(HPT) exit pressure, which is vital to distinguishing failure modes between different turbines. For the case of an abrupt failure occurring in a single turbine component, a model-based sensor measurement reconstruction method is proposed in this paper. First,to estimate the missing measurements, the forward algorithm and the backward algorithm are developed based on corresponding component models according to the failure hypotheses. Then,a new fault diagnosis logic is designed and the traditional nonlinear filter is improved by adding the measurement estimation module and the health parameter correction module, which uses the reconstructed measurement to complete the health parameters estimation. Simulation results show that the proposed method can well restore the desired measurement and the estimated measurement can be used in the turbofan engine gas path diagnosis. Compared with the diagnosis under the condition of missing sensors, this method can distinguish between different failure modes, quantify the variations of health parameters, and achieve good performance at multiple operating points in the flight envelope. 相似文献
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基于鲁棒非线性卡尔曼滤波的自适应SLAM算法 总被引:1,自引:0,他引:1
针对传统非迹卡尔曼滤波算法缺乏在线自适应调整能力,在噪声模型出现误差时滤波精度下降的问题,提出了一种基于鲁棒无迹卡尔曼滤波的同步定位与地图创建算法。该算法引入了一个多维观测噪声尺度因子,能根据观测噪声统计特性的实际变化情况对每种传感器的噪声模型做出自适应调整,使其逼近真实噪声水平,进而将滤波增益调整到一个适当值,实现滤波器的最优估计。SLAM仿真实验结果表明,在噪声统计特性发生变化的情况下,该算法相比其它几种SLAM算法具有更好的自适应能力,估计精度更高,鲁棒性更强。 相似文献
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Eun-Jung Choi Jae-Cheol Yoon Byoung-Sun Lee Sang-Young Park Kyu-Hong Choi 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2010
Spaceborne GPS receivers are used for real-time navigation by most low Earth orbit (LEO) satellites. In general, the position and velocity accuracy of GPS navigation solutions without a dynamic filter are 25 m (1σ) and 0.5 m/s (1σ), respectively. However, GPS navigation solutions, which consist of position, velocity, and GPS receiver clock bias, have many abnormal excursions from the normal error range for space operation. These excursions lessen the accuracy of attitude control and onboard time synchronization. In this research, a new onboard orbit determination algorithm designed with the unscented Kalman filter (UKF) was developed to improve the performance. Because the UKF is able to obtain the posterior mean and covariance accurately by using the second-order Taylor series expansion through the sampled sigma points that are propagated by using the true nonlinear system, its performance can be better than that of the extended Kalman filter (EKF), which uses the linearized state transition matrix to predict the covariance. The dynamic models for orbit propagation applied perturbations due to the 40 × 40 geo-potential, the gravity of the Sun and Moon, solar radiation pressure, and atmospheric drag. The 7(8)th-order Runge–Kutta numerical integration was applied for orbit propagation. Two types of observations, navigation solutions and C/A code pseudorange, can be used at the user’s discretion. The performances of the onboard orbit determination were verified using real GPS data of the CHAMP and KOMPSAT-2 satellites. The results of the orbit determination were compared with the precision orbit ephemeris (POE) of the CHAMP and KOMPSAT-2 satellites. 相似文献
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针对飞行器在高速飞行时受气流干扰、惯性数据易发散等问题,从传感器数据融合角度出发,提出了通过无迹卡尔曼滤波(UKF)融合嵌入式大气数据观测系统(FADS)和惯性导航系统(INS)估计飞行器实时大气数据的算法。算法使用高维度非线性方程对惯性系统和大气系统间的关系建模,结合FADS与INS的数据,计算飞行器速度和高度,进而估算出攻角、侧滑角等参数。实验结果显示,与INS直接解算、扩展卡尔曼滤波(EKF)融合等原有估计方法相比,文章所述的算法在估计精度和系统稳定性方面均有所提高。 相似文献
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基于神经网络的无迹滤波改进算法 总被引:2,自引:0,他引:2
介绍了采用无迹变换(UT)描述随机变量通过非线性系统后的均值及方差的方法,提出可以将神经模糊推理系统(ANFIS)用于确定无迹变换中的参数,使其对随机变量均值的描述达到二次以上精度,并给出了改进的无迹滤波器(UKF)结构和神经网络训练方法;仿真结果表明,该算法适用于系统含有未知输入或系统噪声为非高斯的情况,并可解决一些典型的非线性估计问题,改进算法的性能优于传统无迹滤波器。 相似文献