白噪声驱动的线性系统动态特性测量方法

冲激响应反应了线性系统的内部动态特性。该文详细介绍了系统冲激响应辨识的时域互相关技术。它用白噪声对系统进行驱动,将离散时间线性系统转化为时间序列的ARMA模型,并将基于MA模型的系统辨识问题转化为一个优化问题,以最小二乘标准求解。求解过程利用采样样本估计相关函数,构造对称的Toeplitz矩阵方程,再用迭代的Levinson-Durbin算法解该矩阵方程。最后,文章给出了一个线性系统的测量实例。实验结果表明,该方法能准确的估计线性系统的冲激响应。     

MIMO-OFDMA时变信道和载波频偏联合估计

针对MIMO-OFDMA上行无线通信系统,提出一种基于随机集理论的多用户时变信道和载波频率偏移联合估计算法.该算法基于随机集合理论,利用一个有限随机集合来表示和描述系统中实时动态变化的接入用户状态、多径信道的冲激响应,以及每个无线接入用户对应的载波频率偏移量;然后,利用贝叶斯最优估计理论,给出在随机集模型下MIMO-OFDMA上行无线系统中信道冲激响应和载波频率偏移的最优估计表达式;最后,利用粒子滤波算法逼近求解该随机集模型下的贝叶斯最优估计问题.仿真实验结果表明,在MIMO-OFDMA上行无线系统中接入用户数未知且动态变化等复杂情况下,该算法仍然可以实现对时变信道冲激响应和载波频偏的有效估计.     

基于广义互相关算法的时延估计

在无线电定位技术中,AOA、TOA、TDOA是目前最有发展潜力的蜂窝系统移动台定位技术。定位中对时间差估计的精度要求非常高,测量精度越高,由其引入的定位误差就会越小。传统的广义互相关算法在实际的噪声环境中产生的时延估计误差较高。在此基础上,对时延估计采用了基于周期互谱密度和互功率谱的广义互相关算法进行研究,改进了传统的广义互相关算法。仿真结果表明改进的时延估计算法在恶劣信道环境影响下表现出了相对较好的鲁棒性。     

ZYNQ7020的超声阵列测温系统设计

设计了一种基于ZYNQ7020的超声阵列测温系统，实现对尾焰温度的实时、精确检测。本设计以基于ZYNQ7020的8通道数据采集卡作为中控系统，搭配A/D芯片、放大滤波电路、功率放大电路完成对原始数据的采集。利用并行化多通道A/D数据采集系统设计了并行化收发时序，在ZYNQ中集成了Linux系统，设计DMA内核驱动程序，将采集到的回波数据上传至上位机。利用信号调制及互相关算法、最小二乘法在上位机进行二维温度场的信息重建并获得相应的温度场图像。经过测试，设计的超声测温系统可以有效获取回波信号并对其进行算法处理，完成对二维温度场的重建。     

LTE中一种时频LMMSE信道估计算法

由于3GPP长期演进LTE(Long Term Evolution)系统物理层采用了OFDM技术。为了对高速的OFDM信号进行相干解调,通常需要估计和跟踪衰落信道的特性。在比较了基于参考信号的各种信道估计算法之后,提出了一种适用于LTE系统的信道估计算法。根据PRB(Physical Resource Block)的数目,采用基于SVD分解的频域LMMSE算法估计出频域的信道冲激响应值,再采用时域LMMSE算法估计出时域的信道冲激响应值。仿真结果表明,此方法能有效地降低信道估计MSE值,并在改善BER性能的同时简化了运算复杂度,因而具有较大的实用性。     

基于PN序列的OFDM系统信道估计方法研究

文章介绍了OFDM系统中基于PN序列的信道估计技术,并对基于PN序列的时域迭代相关检测算法进行了改进,即在现有方法的基础上,把得到的信道冲激响应按固定的OFDM长度分块,并对每块符号进行补零、FFT,然后在时频二维平面进行三次样条插值得到每块符号中全部点的频率响应,与改进前及典型块状导频估计算法相比较,提高了估计精度。     

实时混合仿真系统的A/D与D/A转换器

实时混合仿真系统对 A/D 与 D/A 转换器的速度、精度、同步以及抗干扰能力均有较高要求。MSM-14型 A/D 与 D/A转换器利用国产器件设计。已用于三自由度系统实时混合仿真中,目前 MSM-14工作于三台 DMJ-3A(B)模拟计算机及两台 DJS-622数字计算机之间,并通过了上级技术部门鉴定。     

利用二次相关改进的广义互相关时延估计算法

为了提高传统广义互相关时延估计算法的性能,本文将二次相关时延估计算法与广义互相关时延估计算法相结合,提出了一种利用二次相关改进广义互相关的时延估计新算法。该算法先对信号进行二次相关处理,利用相关函数抑制了噪声的干扰,然后在相关函数序列上进行权重处理,进一步提高了算法的抗噪声性能与估算精度。仿真实验证明新算法较广义互相关算法性能有了明显的提升,对闪电辐射源定位资料的分析也验证了该算法的有效性。     

一种新的基于训练序列的MIMO-OFDM信道估计方法

为了实现MIMO-OFDM(多输入多输出—正交频分复用)系统的相干检测,提出一种新的基于训练序列的信道估计方法。详细阐述了算法的估计准则和训练序列的构造方法。利用训练序列良好的相关特性简便、精确估计出信道的频域响应及其冲激响应。通过理论分析和计算机仿真说明,新的算法对比最佳训练序列的LS(最小二乘法)时域估计方法,具有同样的估计精度,同时避免复杂的矩阵求逆运算,使计算复杂度进一步降低。     

新的基于训练序列的MIMO-OFDM时域信道估计方法

为了实现多输入多输出—正交频分复用系统的相干检测,提出一种新的基于训练序列的信道估计方法。将使用的训练序列在时间上呈现正交性,同时利用训练序列本身良好的相关特性简便、精确估计出信道的冲激响应。通过理论分析和计算机仿真证明,新的算法对比最佳训练序列的LS(最小二乘法)时域估计方法,在具有同样估计精度的同时,避免了复杂的矩阵求逆运算,使计算复杂度进一步降低。     

Prediction error identification of linear systems: A nonparametric Gaussian regression approach

A novel Bayesian paradigm for the identification of output error models has recently been proposed in which, in place of postulating finite-dimensional models of the system transfer function, the system impulse response is searched for within an infinite-dimensional space. In this paper, such a nonparametric approach is applied to the design of optimal predictors and discrete-time models based on prediction error minimization by interpreting the predictor impulse responses as realizations of Gaussian processes. The proposed scheme describes the predictor impulse responses as the convolution of an infinite-dimensional response with a low-dimensional parametric response that captures possible high-frequency dynamics. Overparameterization is avoided because the model involves only a few hyperparameters that are tuned via marginal likelihood maximization. Numerical experiments, with data generated by ARMAX and infinite-dimensional models, show the definite advantages of the new approach over standard parametric prediction error techniques and subspace methods both in terms of predictive capability on new data and accuracy in reconstruction of system impulse responses.     

Controller synthesis for sign-invariant impulse response

In this paper, we consider the problem of designing controllers for discrete-time linear time-invariant (LTI) plants that render the closed-loop impulse response nonnegative. Such systems have a non-undershooting and non-overshooting step response. We first show that the impulse response of any discrete-time LTI system changes sign at least "r" times if it has "r" real, positive zeros outside a circular disk centered at the origin and containing all its poles. We then show that a necessary and sufficient condition on the plant for the existence of a compensator that makes the closed loop impulse response sign invariant is that there be no real, positive, nonminimum phase plant zeros. Finally, we show, by construction, how such a compensator may be synthesized when the plant does satisfy the existence condition.     

An optimum time scale for discrete Laguerre network

Any discrete-time stable transfer function can be expressed by a discrete-time Laguerre series with a chosen time scale. An optimum time scale such that an index is minimized is derived. This index ensures that the coefficients of higher-order Laguerre functions go toward zero quickly. The solution derived requires the knowledge of the impulse response of the discrete plant. Cases of first-order plants, second-order underdamped plants, and plants with multiunit delay are also discussed     

A complete model of a finite-dimensional impedance-passive system

We extend the classes of standard discrete- and continuous-time input/state/output matrix systems by adding reverse internal and/or external channels. The reverse internal channel permits the impulse response to contain a differentiating part, and the reverse external channel allows us to include inputs which are forced to be zero and outputs which are undetermined. The purpose of this extension is obtaining a class of state-space matrix systems that can be used to realise all right-coprime positive-real rational relations—in particular non-proper positive-real rational transfer functions can be realised. We generalise the notions of impedance and scattering passivity to extended systems. When we restrict our attention to passive systems, the new class of extended impedance-passive systems is closed under the operations of interchanging the input and the output, as well as frequency inversion and duality. We generalise two system Cayley transformations to extended systems. The first transformation that we consider is the internal Cayley transformation, which maps an impedance- or scattering-passive continuous-time system into a discrete-time approximation of the original system that is again impedance passive or scattering passive, respectively. The second transformation is the external Cayley transformation that maps a contiuous- or discrete-time impedance-passive system into a scattering-passive system with the same time axis. In our extended setting, the two Cayley transformations become bijections between the respective classes of extended passive systems.     

On a general optimal algorithm for multirate output feedbackcontrollers for linear stochastic periodic systems

A modified optimal algorithm for multirate output feedback controllers of linear stochastic periodic systems is developed. By combining the discrete-time linear quadratic regulation (LQR) control problem and the discrete-time stochastic linear quadratic regulation (SLQR) control problem to obtain an extended linear quadratic regulation (ELQR) control problem, one derives a general optimal algorithm to balance the advantages of the optimal transient response of the LQR control problem and the optimal steady-state regulation of the SLQR control problem. In general, the solution of this algorithm is obtained by solving a set of coupled matrix equations. Special cases for which the coupled matrix equations can be reduced to a discrete-time algebraic Riccati equation are discussed. A reducable case is the optimal algorithm derived by H.M. Al-Rahmani and G.F. Franklin (1990), where the system has complete state information and the discrete-time quadratic performance index is transformed from a continuous-time one     

An interative learning control scheme using the weighted least-squares method

An iterative learning control scheme is described for linear discrete-time systems. A weighted least-squares criterion of learning error is optimized to obtain a unique control gain for a case when the number of sampling is relatively small. It is then shown that algorithmic convergence can be readily guaranteed, because the present learning rule consists of a steady-state Kalman filter. By paying attention to the sparse system structure for the system's impulse response model, we further derive a suboptimal iterative learning control for a practical case when the number of sampling is large.     

An alternative FIR filter for state estimation in discrete-time systems

To overcome the resulting problems of existing finite impulse response (FIR) structure filters, this paper proposes an alternative FIR filter for state estimation in discrete-time systems, which is derived from the well-known Kalman filter with recursive infinite impulse response (IIR) structure. The proposed FIR filter obtains a posteriori knowledge about the window initial condition from the most recent finite observations, while existing FIR filters handle this task arbitrarily or heuristically. The gain matrix for the proposed FIR filter incorporates a posteriori knowledge about the window initial condition during its design and is shown to be time-invariant. The proposed FIR filter is shown to have good inherent properties such as unbiasedness and deadbeat. Through extensive computer simulations, the proposed FIR filter can be shown to be comparable with the Kalman filter for the nominal system and better than that for the temporarily uncertain system.     

Robust stability of discrete-time systems using delta operators

The robust stability of discrete-time systems formulated in terms of the delta (δ) operator is discussed. That is, given the nominal characteristic equation P(δ) of a discrete-time system, it is of interest to know how much the coefficients can be perturbed while preserving stability. A procedure to obtain the maximum intervals for a perturbed polynomial P(δ) to still be stable is presented