共查询到19条相似文献,搜索用时 187 毫秒
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关于飞行器航迹优化控制问题,地形跟随飞行控制技术是实现飞行器低空突防的重要技术,以飞行器航迹倾斜角为被控对象,利用地形起伏提高飞行器的战场生存能力.适应角法是地形跟随控制系统中应用最广泛的一种方案.为了提高地形跟随控制精度,优化过顶轨迹,在分析了适应角法中传统抑制函数的不足后,提出了一种模糊抑制函数的改进适应角算法,结合地形跟随飞行中各阶段的不同特点,制定模糊规则来计算抑制函数,使得飞行器能够快速响应地形起伏变化.同时,结合飞行器运动模型,对地形跟随的效果进行了仿真.仿真结果表明,采用模糊抑制函数的地形跟随控制系统能够减小过顶超调,增强飞行隐蔽性.证明所设计的模糊抑制函数能达到更好的地形跟随轨迹控制效果. 相似文献
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航迹规划是低空突防过程中的关键技术,目的是得到一条既安全可靠又全局代价最优的三维航迹,是导航系统和控制系统的基础之一.在对整体航迹规划技术进行研究与实现的过程中,详细讨论了威胁建模、地形预处理、航迹规划算法等问题,并采用建模/仿真技术给出了实现流程以及试验结果.规划出的参考航迹能够满足低空突防飞行器进行"地形跟随/地形回避/威胁回避"机动飞行的需要.随着航迹规划问题的解决,建立了"地形跟随/地形回避/威胁回避"为主要手段的低空突防技术论证与试验环境. 相似文献
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为了提高无人机的低空滑翔抗攻击突防和控制能力,提出一种基于快速模型预测的无人机低空滑翔抗攻击突防控制技术。采用融合传感识别技术进行无人机的姿态和位置参数信息采集,分析无人机的低空滑翔控制的物理环境参数模型,构建无人机飞行轨迹地图模型,使用标准卡尔曼滤波器进行无人机低空滑翔抗攻击突防控制信息的融合处理,根据信息融合结果进行控制指令设计。采用动态基元轨迹跟踪方法,得到无人机低空突防控制的滑模面,在有限Morrey空间内采用串联弹性驱动控制方法求得在控制约束参量分布模型的最优解。根据无人机低空突防段的初始位姿参数进行快速模型预测和飞行轨迹跟踪,实现低空滑翔抗攻击突防控制。仿真结果表明,采用该方法进行无人机低空滑翔抗攻击突防控制的精度较高,无人机的姿态参数的自适应调节性能较好。 相似文献
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研究飞行器多目标优化问题,为了寻求安全突防的飞行轨迹,结合低空突防中对飞行器实际飞行轨迹的要求,对航迹规划算法进行了研究.针对改善目前航迹规划过程中所存在的几何建模困难和所得轨迹不能符合实际可飞的问题,提出了一种新的垂直面轨迹规划方法.方法在水平面规划的基础上重点分析了飞行器垂直面内的运动状态,结合过载、航迹倾角等机动性约束条件对垂直面内的运动轨迹进行研究,将实际复杂的飞行轨迹简化为由直线段和圆弧段衔接而成的轨迹模型,针对简化模型进行轨迹规划.仿真结果表明,能够得到满足约束条件,并符合实际飞行的航迹. 相似文献
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在间歇过程的状态估计中,如何充分利用多批次重复特性信息是一个挑战。迭代学习卡尔曼滤波方法利用卡尔曼滤波沿时间方向估计相邻两批次之间的状态误差,并沿批次方向迭代更新当前状态估计,兼顾了时间和批次两维特性。但是,这种方法只适用于线性系统。针对非线性间歇过程,提出一种迭代学习拟线性卡尔曼滤波器(ILQKF)方法。ILQKF基于间歇过程的标称模型,将实际状态与标称状态之间的误差作为新状态,建立了与误差相关的线性化模型。然后,根据迭代学习卡尔曼滤波方法,对状态误差进行估计,而状态轨迹为误差轨迹与标称轨迹之和,从而估计出非线性间歇过程的状态。啤酒发酵过程的应用仿真验证了ILQKF方法的优越性。 相似文献
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The application of nonlinear Kalman filtering techniques to the continuous updating of an inertial navigation system using individual radar terrain-clearance measurements has been investigated. During this investigation, three different approaches for handling the highly nonlinear terrain measurement function were developed and their performance was established. These were 1) a simple first-order extended Kalman filter using local derivatives of the terrain surface, 2) a modified stochastic linearization technique which adaptively fits a least squares plane to the terrain surface and treats the associated fit error as an additional noise source, 3) a parallel Kalman filter technique utilizing a bank of reduced-order filters that was especially important in applications with large initial position uncertainties. Theoretical and simulation results are presented. 相似文献
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为了提高四旋翼无人机对地面目标跟踪的稳定性和跟踪精度,提出了一种结合Tiny-YOLOV3和卡尔曼滤波的跟踪算法;首先分析了Tiny-YOLOV3的原理和网络结构,并基于Tiny-YOLOV3的目标检测结果,结合无人机状态和目标的几何关系建立了目标跟踪系统的数学模型;接着对目标相对运动关系进行分析,建立目标的运动学模型,考虑到目标检测结果受干扰影响较大,应用卡尔曼滤波器实现对目标轨迹的滤波和预测,进而提升目标跟踪的精度;最后根据经过卡尔曼滤波后的目标轨迹信息设计无人机控制律,在轨迹控制的同时引入对无人机偏航角的控制,从而实现无人机对目标的稳定跟踪;仿真结果表明无人机对目标的位置跟踪精度在0.5 m以内,速度跟踪误差在0.2 m/s以内,偏航角跟踪误差在3°以内,跟踪效果良好,从而论证了所提算法的有效性。 相似文献
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For continuous-time nonlinear deterministic system models with discrete nonlinear measurements in additive Ganssian white noise, the extended Kalman filter (EKF) convariance propagation equations linearized about the true unknown trajectory provide the Cramér-Rao lower bound to the estimation error covariance matrix. A useful application is establishing the optimum filter performance for a given nonlinear estimation problem by developing a simulation of the nonlinear system and an EKF linearized about the true trajectory. 相似文献
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Kangwagye Samuel JaeWeon Choi 《International Journal of Control, Automation and Systems》2018,16(6):2763-2771
We propose a method of improving tracking filter performance of a highly maneuvering target with mixed system noises in this paper. A case study of an off-road high speed moving target is considered. The system noises consist of white Gaussian noises generated from target motion models and additional colored noises arising from the effect of rough and uneven terrain profile. we design the colored noise first order discrete Markov dynamic system representing terrain conditions. Tracking is done by using an IMM filter with discrete white noise acceleration and horizontal coordinated turn models. The designed colored noise dynamic model is augmented with each of the motion models. We use Kalman filter for linear DWNA model while extended and unscented Kalman filters are used for nonlinear HCT model. A test scenario is setup and simulations are carried out. For filter performance comparison purposes, two more cases are considered i.e., systems with white noncorrelated system noises and the system correlated noise cases. Results show that the proposed method outperforms the traditional error treatment methods in terms of robustness, small mean square error, and acceptable computation load and data processing time. 相似文献
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对于带未知有色观测噪声的多传感器线性离散定常随机系统, 未知模型参数和噪声方差的一致的融合估值器用递推增广最小二乘法(RELS)和求解相关函数方程得到. 将这些估值器代入到最优解耦融合Kalman滤波器中, 得出了自校正解耦融合Kalman滤波器, 并用动态方差误差系统分析(DVESA)和动态误差分析(DESA)方法证明了它收敛于最优解耦融合Kalman滤波器, 因而具有渐近最优性. 一个带3传感器跟踪系统的仿真例子说明了其有效
性. 相似文献
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For multisensor systems, when the model parameters and the noise variances are unknown, the consistent fused estimators of the model parameters and noise variances are obtained, based on the system identification algorithm, correlation method and least squares fusion criterion. Substituting these consistent estimators into the optimal weighted measurement fusion Kalman filter, a self-tuning weighted measurement fusion Kalman filter is presented. Using the dynamic error system analysis (DESA) method, the convergence of the self-tuning weighted measurement fusion Kalman filter is proved, i.e., the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a realization. Therefore, the self-tuning weighted measurement fusion Kalman filter has asymptotic global optimality. One simulation example for a 4-sensor target tracking system verifies its effectiveness. 相似文献