排序方式: 共有70条查询结果,搜索用时 406 毫秒
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基于BP人工神经网络的GPS/SINS组合导航算法 总被引:1,自引:0,他引:1
基于扩展Kalman滤波的GPS/SINS组合导航算法,需要对原始的非线性连续系统模型进行线性化和离散化处理,要求系统噪声和测量噪声为零均值的高斯白噪声,且易于出现滤波器发散。BP人工神经网络无需对所求解的问题建模,能够很好地逼近系统非线性特性,获得较高精度的导航定位信息;还具有计算过程稳定,不涉及矩阵求逆,不需要迭代逼近,以及容易实现并行处理等优点。设计适用于GPS/SINS组合导航系统的BP网络模型,并在标准的BP算法基础上,采用共轭梯度法改进网络训练速度及精度。最后,通过仿真算例说明BP网络方法用于GPS/SINS组合导航计算的可行性。 相似文献
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面向典型任务的有人/无人机协同效能评估 总被引:1,自引:0,他引:1
有人/无人机协同作战是C4ISR体系下的一种重要形式。本文以有人/无人机协同执行典型任务为研究背景,针对构建可靠、全面的有人/无人机协同效能理论评估方法的问题展开深入研究。首先分析了未来有人/无人机的协同模式和运用规则;然后采用协同系统综合指数模型,在单机能力模型的基础上,提出了一种有人/无人机编队协同效能评估方法;最后基于Xsim仿真系统平台在典型任务下,通过针对确定机型的多种编队组合仿真推演,将协同效能仿真结果与理论计算结果进行分析对比,协同效能排序的一致性验证了该理论评估方法具有一定的可靠性与可用性。可以预见,未来战争有人/无人机的协同作战将被广泛应用。 相似文献
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《防务技术》2020,16(4):846-855
Aiming at the problem that the traditional Unscented Kalman Filtering (UKF) algorithm can’t solve the problem that the measurement covariance matrix is unknown and the measured value contains outliers, this paper proposes a robust adaptive UKF algorithm based on Support Vector Regression (SVR). The algorithm combines the advantages of support vector regression with small samples, nonlinear learning ability and online estimation capability of adaptive algorithm based on innovation. Firstly, the SVR model is trained by using the innovation in the sliding window, and the new innovation is monitored. If the deviation between the estimated innovation and the measured innovation exceeds a given threshold, then measured innovation will be replaced by the predicted innovation, and then the processed innovation is used to calculate the measurement noise covariance matrix using the adaptive estimation algorithm. Simulation experiments and measured data experiments show that SVRUKF is significantly better than the traditional UKF, robust UKF and adaptive UKF algorithms for the case where the covariance matrix is unknown and the measured values have outliers. 相似文献
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VXI总线在导弹综合测试系统的应用 总被引:1,自引:0,他引:1
介绍了VXI总线系统的技术特点 ,阐述VXI总线技术在导弹综合测试系统的应用情况 ,详细描述了利用VXI总线组建的导弹测试系统的硬件及应用软件的设计 ,指出VXI总线技术在国防武器装备中具有广泛的应用前景。 相似文献
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《防务技术》2020,16(6):1130-1141
Based on fuzzy adaptive and dynamic surface (FADS), an integrated guidance and control (IGC) approach was proposed for large caliber naval gun guided projectile, which was robust to target maneuver, canard dynamic characteristics, and multiple constraints, such as impact angle, limited measurement of line of sight (LOS) angle rate and nonlinear saturation of canard deflection. Initially, a strict feedback cascade model of IGC in longitudinal plane was established, and extended state observer (ESO) was designed to estimate LOS angle rate and uncertain disturbances with unknown boundary inside and outside of system, including aerodynamic parameters perturbation, target maneuver and model errors. Secondly, aiming at zeroing LOS angle tracking error and LOS angle rate in finite time, a nonsingular terminal sliding mode (NTSM) was designed with adaptive exponential reaching law. Furthermore, combining with dynamic surface, which prevented the complex differential of virtual control laws, the fuzzy adaptive systems were designed to approximate observation errors of uncertain disturbances and to reduce chatter of control law. Finally, the adaptive Nussbaum gain function was introduced to compensate nonlinear saturation of canard deflection. The LOS angle tracking error and LOS angle rate were convergent in finite time and whole system states were uniform ultimately bounded, rigorously proven by Lyapunov stability theory. Hardware-in-the-loop simulation (HILS) and digital simulation experiments both showed FADS provided guided projectile with good guidance performance while striking targets with different maneuvering forms. 相似文献
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根据国外吸气式超声速战术导弹一体化外形设计取得的最新进展 ,着重分析了这类导弹外形设计的特点和技术关键 ,提出了吸气式远程超声速防空导弹一体化外形设计中应解决好的几个重大技术问题 ,以期引起各方对吸气式防空导弹总体设计中有关问题的关注和讨论 ,共同寻找解决这些技术问题的有效途径 相似文献
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《防务技术》2020,16(2):334-340
In view of the failure of GNSS signals, this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network (RNN). This proposed method utilizes the calculation principle of INS and the memory function of the RNN to estimate the errors of the INS, thereby obtaining a continuous, reliable and high-precision navigation solution. The performance of the proposed method is firstly demonstrated using an INS/GNSS simulation environment. Subsequently, an experimental test on boat is also conducted to validate the performance of the method. The results show a promising application prospect for RNN in the field of positioning for INS/GNSS integrated navigation in the absence of GNSS signal, as it outperforms extreme learning machine (ELM) and EKF by approximately 30% and 60%, respectively. 相似文献
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