ID 原文 译文
48266 该算法通过旋转发射阵列天线得到不同方位角的协方差矩阵,利用子空间算法估计通道的增益和相位误差。 The algorithm is obtained by rotating emission antenna array covariance matrix of different azimuth, subspace algorithm is used to estimate channel gain and phase error.
48267 该算法无需已知直达波的入射角,只需已知发射阵列天线的旋转角度,即可同时完成发射通道幅相误差和直达波到达角的估计,且性能较好,计算机仿真和实测数据结果验证了算法的有效性。 No known direct wave incident Angle, the algorithm only known emission antenna array rotation Angle, can be completed at the same time transmission channel amplitude-phase error and direct wave arrival Angle estimates, and good performance, computer simulation and measured data results demonstrate the effectiveness of the algorithm.
48268 研究了一类非匹配不确定高阶非线性系统的跟踪控制问题。 For a class of unmatched uncertain high-order nonlinear systems in the tracking control problem.
48269 基于自适应增加幂次积分递推设计方法,利用基神经网络的逼近特性,提出了一种自适应神经网络增加幂次积分动态面设计方法。 Based on adaptive increase the exponential integral recursive design method, using the basis neural network approximation properties, puts forward an adaptive neural network to increase the exponential integral dynamic surface design method.
48270 在每个子系统中,采用双极Sigmoid函数设计期望虚拟控制律,保证了其可导性;引入一阶滤波器,避免了对期望虚拟控制律的微分。 In each subsystem, the use of a bipolar Sigmoid function expected virtual control law design, to ensure its conductivity;The first order filter is introduced to avoid the differential of the expected virtual control law.
48271 仿真实例表明,所提控制方法能够保证闭环高阶非线性系统的状态量和跟踪误差半全局一致终结有界。 Simulation examples show that the proposed control method can guarantee the closed-loop high order nonlinear system state variable and global asymptotical end half tracing error to be bounded.
48272 传统交互多模型(interacting multiple model,IMM)滤波算法中,马尔可夫概率转移矩阵参数固定,切换过程模型概率滞后。 The traditional interacting multiple model (interacting multiple model, the IMM) filtering algorithm, the markov probability transfer matrix parameters fixed, hysteresis switching process model probability.
48273 基于后验信息修正,扩展了一种在线更新马尔可夫概率转移矩阵的自适应跟踪算法,新算法克服了原算法只能交互2个模型的局限性。 Based on a posteriori information correction, extends an online update markov probability transfer matrix of the adaptive tracking algorithm, the new algorithm overcomes the original algorithm can interact only two limitations of the model.
48274 在计算过程中,依据不匹配模型误差压缩率的更新信息,在线调整先验马尔可夫概率转移矩阵,模型转换过程中更多地利用匹配模型的信息,而减小不匹配模型信息的影响,使收敛速度得到了提高。 In the process of calculation, based on the compression ratio of update information does not match the model error, adjust the prior probability of markov transition matrix online, more in the process of model transformations using matching model of information, and reduce the influence of information does not match the model, the convergence speed is improved.
48275 最后通过多模交互3个当前统计模型(current statistical model,CSM)验证了所提算法的有效性。 The last three interact through multimode current statistical model (current statistical model, the CSM) to verify the effectiveness of the proposed algorithm.