Robust state estimation for uncertain linear systems with deterministic input signals

In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model. The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity. Under a few weak assumptions, it is proved that though the derived state estimator is biased, the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded. Numerical simulations show that the obtained robust filter has relatively nice estimation performances.     

一种新的InSAR基线估计方法

针对InSAR技术中基线估计误差对DEM精度的影响,从传统的基线估计方法入手,提出了一种新的估计基线参数的方法,并用仿真实验做了论证.结果表明,该方法比以往传统的方法得到的基线参数的精度更高,运算速度更快,应用范围更加广泛.     

A practical robust nonlinear controller for maglev levitation system

In order to explore the precise dynamic response of the maglev train and verify the validity of proposed controller, a maglev guideway-electromagnet-air spring-cabin coupled model is developed in the first step. Based on the coupled model, the stresses of the modules are analyzed, and it is pointed out that the inherent nonlinearity, the inner coupling, misalignments between the sensors and actuators, and external disturbances are the main issues that should be considered for the maglev engineering. Furthermore, a feedback linearization controller based on the mathematical model of a maglev module is derived, in which the nonlinearity, coupling and misalignments are taken into account. Then, to attenuate the effect of external disturbances, a disturbance observer is proposed and the dynamics of the estimation error is analyzed using the input-to-state stability theory. It shows that the error is negligible under a low-frequency disturbance. However, at the high-frequency range, the error is unacceptable and the disturbances can not be compensated in time, which lead to over designed fluctuations of levitation gap, even a clash between the upper surface of electromagnet and lower surface of guideway. To solve this problem, a novel nonlinear acceleration feedback is put forward to enhancing the attenuation ability of fast varying disturbances. Finally, numerical comparisons show that the proposed controller outperforms the traditional feedback linearization controller and maintains good robustness under disturbances.     

双选择瑞利信道MIMO-OFDM存在信道估计误差分析

双选择性(时域多变性和频域选择性)瑞利信道多入多出-正交频分复用(MIMO-OFDM)系统中,信道估计误差客观存在.为了研究双选择性瑞利信道中信道估计误差对MIMO-OFDM系统的影响,使用迫零检测和对Wishart分布的近似处理,从理论上推导出了存在信道估计误差时双选择不相关瑞利信道和双选择指数相关瑞利信道中MIMO-OFDM系统信干噪比(SINR)的概率分布.根据系统SINR概率分布,推导出了存在信道估计误差时系统的平均差错率和中断概率.仿真结果表明了理论分析的正确性.     

全双工大规模MIMO网络中最优天线分配

在全双工大规模MIMO网络中,为了降低自干扰,提升频谱效率,提出了一种最优天线分配算法。该算法在基站采用迫零线性信号处理和存在信道估计误差的前提下,首先推导出系统和速率的紧下界。通过分析该下界,以系统和速率最大化为优化目标,得到最优的收发天线比。理论分析和仿真结果表明:1）收发天线比与上行导频信号发射功率成正比,与下行导频信号发射功率成反比;2）当天线数目无穷大时,最优天线比趋近于1。     

基于剪枝优化的最优波束序列快速搜索方法

为解决多波束天线典型测试波束组合数量大、遍历效率低、最优波束序列搜索代价极大的问题,通过分析多波束天线激励系数矩阵中波束与馈源阵列的映射关系,结合合成多波束的形成原理,采用最小二乘估计法构建馈源阵列的平均估计误差判定标准,并在建立典型测试波束选取的无关性、覆盖性和馈源贡献率准则的基础上,提出一种基于剪枝优化设计的最优波束序列快速搜索算法.通过仿真得出了实际条件下的最优测试波束序列.实验结果表明,优化后的搜索算法复杂度降低,搜索结果准确,效率提高显著,对在实际系统中实施高效、准确的最优波束序列快速搜索具有重要的参考价值.     

Fault diagnosis based on high-gain observer with an update law for a class of nonlinear systems

In this paper, by using the well-known high-gain observer design, an update law for the gain and an adaptive estimation of parameters, a new method of fault diagnosis for a class of nonlinear systems is presented. Without resort to any transformation for the parameters, the estimation errors of the states and the parameters are guaranteed to be globally exponentially convergent by a persistent excitation condition. Compared to the existing results, it can be applied to nonlinear systems with nonlinear terms admitting an incremental rate depending on the measured output. A case study further verifies the validity of the proposed research.     

一种加权均方误差最小化的鲁棒性干扰对齐算法

线性干扰对齐的一个常见优化目标是总传输速率最大化,但因为和速率函数的非凸特性而难以直接求解。加权均方误差最小化算法借助均方误差与和速率之间的等价关系解决了这一问题。这一方法需要获得准确的信道状态信息,在实际应用中,通道估计误差的存在会导致算法性能的下降。该文提出一种改进算法,在干扰对齐预编码矩阵与接收矩阵的优化求解过程中将通道估计误差的统计特性考虑在内。仿真结果表明,相比以往的加权均方误差最小化算法,该文算法对信道估计误差具有较高的鲁棒性,可以有效提高总的传输速率。     

High SNR sum capacity analysis of BD MIMO BC systems with imperfect CSI

We investigate the sum capacity of Block Diagonalization precoding Multiple Input Multiple Output Broadcast Channels (BD MIMO BC) with imperfect Channel State Information (CSI) at the base station. Since it is difficult to obtain the exact expression, a lower and an upper bounds of the sum capacity under Gaussian channel estimation errors are drived instead. Analyses show that the gap between two bounds is considerably tight at all Signal to Noise Ratio (SNR) region. From the lower bound of the sum capacity, we can see that the multiplexing gain tends to be zero at high SNR region, which indicates that the BD MIMO BC system with channel estimation errors is interference-limited at high SNR.