MB-OFDM UWB系统中的加窗算法设计

提出一种能抵抗超宽带系统中强窄带干扰的接收端加窗算法,它适用于多带正交频分复用(MB-OFDM)超宽带通信系统中采用的零值填充机制。相对于其他抗窄带干扰算法,接收端加窗算法具有算法复杂度低、无须对窄带干扰进行预估计等优点。在不同强窄带干扰源下的系统无线数据包错误率的仿真测试验证了该算法的有效性和可行性。     

基于ML准则的UWB信号同步捕获方法*

为了在超宽带通信系统中,简单、快速地实现信号的同步捕获和信道估计,提出一种首先利用相关器粗略估计出有用信号的起始位置,然后采用最大似然估计准则实现同步捕获和信道估计的分步同步捕获方法.该方法既提高了估计精度,又有效降低了接收机的复杂度和处理时间.     

基于压缩感知的无线阵列及协同信号处理

传感器阵列信号处理是目标监测重要手段之一,传感器节点向后端数据融合中心发送原始信号不可避免地导致传输延时大、能耗高等问题.为实现低功耗、高精度、灵活部署的目标监测,提出了基于压缩采样的无线阵列.借助新兴的压缩感知理论,解决低功耗低速率无线通信难以满足阵列原始信号的实时传输的问题,并保证阵列测向性能;同时根据相邻节点信号的相关性,设计了基于模型先验知识的信号协同重构算法,以较低的运算负荷完成信号的重构.仿真表明基于压缩感知的无线阵列能实现有效的目标测向,同时在数据量严重受限时性能明显优于传统方法.最后,通过简易实验验证了该方法在低成本平台上的可行性.     

基于Teager-Kaiser算子的差分码移参考超宽带接收机

为提升差分码移参考DCSR(Differential Code Shifted Reference)超宽带接收机在窄带干扰环境中的接收性能,提出了一种基于Teager-Kaiser算子(TKO)的改进型DCSR接收机结构。通过TKO的非线性处理,可以使窄带干扰能量集中在直流附近的低频段,从而可以通过一个模拟高通滤波器将其滤除。仿真结果表明本文提出的改进结构可以有效地抑制窄带干扰,并且在不存在窄带干扰的环境中其性能仍优于传统的差分码移参考接收机和传输参考接收机。改进后的TKO-DCSR性能提升明显且实现复杂度增加不多,可以较好地满足无线传感器网络的应用需求。     

Regularized estimation of bathymetry and water quality using hyperspectral remote sensing

Coastal water mapping from remote-sensing hyperspectral data suffers from poor retrieval performance when the targeted parameters have little effect on subsurface reflectance, especially due to the ill-posed nature of the inversion problem. For example, depth cannot accurately be retrieved for deep water, where the bottom influence is negligible. Similarly, for very shallow water it is difficult to estimate the water quality because the subsurface reflectance is affected more by the bottom than by optically active water components.

Most methods based on radiative transfer model inversion do not consider the distribution of targeted parameters within the inversion process, thereby implicitly assuming that any parameter value in the estimation range has the same probability. In order to improve the estimation accuracy for the above limiting cases, we propose to regularize the objective functions of two estimation methods (maximum likelihood or ML, and hyperspectral optimization process exemplar, or HOPE) by introducing local prior knowledge on the parameters of interest. To do so, loss functions are introduced into ML and HOPE objective functions in order to reduce the range of parameter estimation. These loss functions can be characterized either by using prior or expert knowledge, or by inferring this knowledge from the data (thus avoiding the use of additional information).

This approach was tested both on simulated and real hyperspectral remote-sensing data. We show that the regularized objective functions are more peaked than their non-regularized counterparts when the parameter of interest has little effect on subsurface reflectance. As a result, the estimation accuracy of regularized methods is higher for these depth ranges. In particular, when evaluated on real data, these methods were able to estimate depths up to 20 m, while corresponding non-regularized methods were accurate only up to 13 m on average for the same data.

This approach thus provides a solution to deal with such difficult estimation conditions. Furthermore, because no specific framework is needed, it can be extended to any estimation method that is based on iterative optimization.     

码移参考超宽带收发机及其在WSN环境中的性能分析

介绍了一种新型的基于码移参考技术的超宽带收发机,通过在MATLAB上建模仿真,分析比较了在无线传感器网络应用环境下码移参考超宽带系统的误比特性能。仿真结果表明,采用码移参考技术的超宽带接收机具有比传统的传输参考接收机更好的抗窄带干扰性能,并具有较低的实现复杂度,更适合于无线传感器网络的应用。此外,偏移码字的选择对系统的抗窄带干扰性能有较大影响,采用持续时间短或者随机化特性好的码字,系统的抗窄带干扰能力较强。这些结论为码字的优化设计和系统性能的改进提供了依据。     

Two-stage subspace identification for softsensor design and disturbance estimation

Softsensors or virtual sensors are key technologies in industry because important variables such as product quality are not always measured on-line. In the present work, two-stage subspace identification (SSID) is proposed to develop highly accurate softsensors that can take into account the influence of unmeasured disturbances on estimated key variables explicitly. The proposed two-stage SSID method can estimate unmeasured disturbances without the assumptions that the conventional Kalman filtering technique must make. Therefore, it can outperform the Kalman filtering technique when innovations are not Gaussian white noises or the characteristics of disturbances do not stay constant with time. The superiority of the proposed method over the conventional methods is demonstrated through numerical examples and application to an industrial ethylene fractionator.     

基于性能计数事件的计算机功耗估算模型

精准快速获取计算机系统的实时功耗是功耗优化研究的基础,因此提出并建立了一种高精度的计算机功耗估算模型。通过分析统计系统运行时代表性的性能计数事件,应用机器学习理论分析性能事件与功耗的关系,建立多核计算机系统实时功耗估算模型。模型构建时使用多元线性回归(Multiple Linear Regression,MLR)方法以及支持向量回归(Support Vector Regression,SVR)方法分析二者关系,并对两种方法建立的功耗估算模型进行了对比分析。实验结果表明,基于性能事件的功耗估算模型可准确估计计算机实时功耗,估算误差不高于3%。同已有模型相比较,该估算模型精度更高、通用性更好。     

Towards robust and efficient device-free localization using UWB sensor network

Device-free localization (DFL) is the method of using distributed wireless sensors to localize the target without carrying any devices. Existing DFL methods leverage the variation of narrowband received signal strength (NRSS) which is vulnerable to multipath fading, and thus results in considerable performance degradation in indoor environments. Moreover, the inefficient sensor deployment of traditional DFL involves huge human efforts, which is not suitable for emergency scenarios. In this paper, we utilize sensors transmitting ultra-wideband (UWB) signals to solve both problems. We proposed two RSS variation estimation methods based on the channel impulse response (CIR) measurements provided by UWB sensors, which turn out to be more robust to multipath than NRSS due to the fine multipath resolution of UWB signals. We also employ a higher operating frequency to enhance the shadowing loss for mitigating the multipath effect. Additionally, satisfactory sensor self-localization is achieved under the cooperative localization framework owing to the accurate ranging capability of UWB sensors. We conducted experiments in three different environments to explore the feasibility of our method. The results show that the proposed method gains much better localization performance and requires less human efforts than narrowband DFL.     

基于时间平滑和Toeplitz矩阵重构的复相干信号DoA估计

针对相干多径环境,分别利用时间平滑和Toeplitz矩阵重构算法实现了复相干信号的波达方向(DoA)估计,并进一步通过数学分析和计算机仿真对这两种算法的性能进行了对比,得出结论：时间平滑算法和Toeplitz矩阵重构算法对复相干信号的DoA估计均具有效性,Toeplitz矩阵重构算法性能相对较好,但有一定阵列孔径损失;而基于时间平滑算法无阵列孔径损失,可估计M-1个多径信号(M为阵元数),但计算量偏大。     

Pulse shaping method to compensate for antenna distortion in ultra-wideband communications

In the ultra-wideband (UWB) communication systems, a critical spectral mask is released to restrict the allowable interference to other wireless devices by the Federal Communications Commission (FCC), and then some pulse shaping methods have been presented to fulfil the mask. However, most pulse shaping methods do not consider the antenna distortion which cannot be neglected in the UWB communication systems compared with the conventional systems. To this end, an orthogonal wavelet based pulse shaping method is proposed in this paper to integrate compensation of antenna distortion into pulse shaping. Simulation results show that the novel pulse shaping method can be used to achieve compensation for antenna distortion, optimization of transmission power spectrum, and simplification of the algorithm, as well as simple implementation of the pulse generator.     

超宽带同轴大功率八路功分器设计

针对传统大功率多路功分器带宽窄体积大的问题，提出了利用四分之一波长阶梯阻抗变换原理，设计超宽带的多路功分器的方法。这种设计方法不仅解决了多路功分器各级之间互联线造成的带宽变窄的问题，而且还利用互联线实现了超宽带的阻抗匹配。最后采用上述方法设计了工作在0．8-6．oGHz频带内的超宽带八路功分器，获得了的驻波比小于1．4，采用同轴腔体结构，插损小于0．8dB，完全可以满足电子对抗工程中对于超宽带大功率合成的需要。     

Tracking of Nonstationary Noise Based on Data-Driven Recursive Noise Power Estimation

This paper considers estimation of the noise spectral variance from speech signals contaminated by highly nonstationary noise sources. The method can accurately track fast changes in noise power level (up to about 10 dB/s). In each time frame, for each frequency bin, the noise variance estimate is updated recursively with the minimum mean-square error (mmse) estimate of the current noise power. A time- and frequency-dependent smoothing parameter is used, which is varied according to an estimate of speech presence probability. In this way, the amount of speech power leaking into the noise estimates is kept low. For the estimation of the noise power, a spectral gain function is used, which is found by an iterative data-driven training method. The proposed noise tracking method is tested on various stationary and nonstationary noise sources, for a wide range of signal-to-noise ratios, and compared with two state-of-the-art methods. When used in a speech enhancement system, improvements in segmental signal-to-noise ratio of more than 1 dB can be obtained for the most nonstationary noise sources at high noise levels.     

A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

This paper explores multiple model adaptive estimation (MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter-multiple model adaptive estimation unscented Kalman filter (MMAE-UKF) rather than conventional Kalman filter methods, like the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters, which the improved filtering method can overcome. Meanwhile, this algorithm is used for integrated navigation system of strapdown inertial navigation system (SINS) and celestial navigation system (CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.      

Iterative feedback tuning for Hamiltonian systems based on variational symmetry

This paper proposes a novel iterative feedback tuning (IFT) for Hamiltonian systems, which can describe a practically important class of nonlinear systems. Hamiltonian systems have a special property called variational symmetry, and it can be used to estimate the input‐output mapping of the variational adjoint for certain input‐output mappings of the systems. First, we derive a modified variational symmetry to adapt to the gradient estimation of an optimal control–type cost function with respect to adjustable parameters of a controller. Second, we provide an IFT algorithm based on the property, which generates the optimal parameters minimizing the cost function by iteration of experiments. The proposed algorithm requires less number of experiments to estimate the gradient than the conventional IFT methods for nonlinear systems. We also provide a method to optimize the elements of the dissipation matrix, which does not directly appear in the Hamiltonian function, by equipping a dynamic feedback of the generalized coordinate. Moreover, we provide an IFT algorithm considering parameter constraints so that the parameters can be optimized within a prescribed search range. Finally, a numerical simulation of a two‐link robot manipulator including a comparison with the conventional IFT methods demonstrates the effectiveness of the proposed method.     

A quantile estimation for massive data with generalized Pareto distribution

This paper proposes a new method of estimating extreme quantiles of heavy-tailed distributions for massive data. The method utilizes the Peak Over Threshold (POT) method with generalized Pareto distribution (GPD) that is commonly used to estimate extreme quantiles and the parameter estimation of GPD using the empirical distribution function (EDF) and nonlinear least squares (NLS). We first estimate the parameters of GPD using EDF and NLS and then, estimate multiple high quantiles for massive data based on observations over a certain threshold value using the conventional POT. The simulation results demonstrate that our parameter estimation method has a smaller Mean square error (MSE) than other common methods when the shape parameter of GPD is at least 0. The estimated quantiles also show the best performance in terms of root MSE (RMSE) and absolute relative bias (ARB) for heavy-tailed distributions.     

一种载频信息未知的目标方位估计快速算法

针对载频未知目标方位估计方法的二维搜索运算量大的问题,提出一种基于分离变量方法的目标方位估计新方法,该方法通过泰勒展开将阵列的导向矢量近似为仅含有方位因子的矩阵和仅含有频率因子的向量的积,并通过矩阵变换将二维MUSIC方位估计的空间谱函数中的频率因子约去,在此基础上进行目标的方位估计。整个计算过程只需进行一维搜索即可获得多个目标的方位,有效地减少了原二维搜索算法的计算量。仿真结果表明:该方法高信噪比条件下均方根误差约为0.3°,可以满足工程需要。     

A piecewise regression analysis with automatic change-point detection

Most of the conventional regression methods can only estimate a piecewise polynomial function in which the exact positions or the probabilistic distribution of the change-points is prespecified. This paper proposes an optimization method to estimate a piecewise polynomial function with unknown change-points. We first express a piecewise function by the addition of some absolute terms. Utilizing the properties of this function, a piecewise regression model is then formulated to minimize the estimation errors subjected to an amount of change-points. The model is then solved by a modified goal programming technique, which is more computationally efficient than conventional goal programs. Numerical examples demonstrate that the proposed method is very promising in estimating the piecewise regression with automatic change-point detection.     

基于时序描述逻辑的UML顺序图形式化研究

在统一建模语言（UML）规范中顺序图的语义是以自然语言的形式描述的,是一种半形式化的语言,不能对系统的交互行为进行形式化分析及论证.针对UML顺序图缺乏精确的形式化描述问题,根据顺序图的时序特征,提出了增加交互操作符的UML顺序图的六元组形式化方法.对描述逻辑进行时序扩展,得到可表示动态和时序语义的形式化规范——时序描述逻辑.应用时序描述逻辑的时态算子得到时序描述逻辑语义形式的UML顺序图.用UML顺序图描述完整的C语言执行过程,将其形式化描述,实验结果表明,这种方法是可行的.     

机场场道道面结构层电磁特性反演的辨识方法

研究了基于系统辨识方法的机场场道道面介质层电磁特性的反演问题.在传统系统辨识法反演层状介质电磁特性的基础上,对电磁波在地下介质层中传输时传输模型的建立及回波时延的估计方法作了改进.新方法传输模型中考虑了发射带宽内各频率分量对介质层介电常数虚部的影响,同时引入超分辨率时延估计WRELAX算法估计回波信号的时延.实现了具有薄层的道面介质层电磁特性及其厚度的反演,并使道面下各介质层电磁特性的反演精度有所提高.