Mutual and conditional mutual informations for optimizingdistributed Bayes detectors

This paper considers optimization of distributed detectors under the Bayes criterion. A distributed detector consists of multiple local detectors and a fusion center that combines the local decision results to obtain a final decision. Introduced first are distributional distance measures, the mutual information (MI) and the conditional mutual information (CMI), that are obtained by applying information theoretic concepts to detection problems. Error bound analyses show that these distance measures approximate the Bayesian probability of error better than the conventional ones regardless of the operational environments. Then, a new optimization technique is proposed for distributed Bayes detectors. The method uses the distributional distances instead of the original Bayes criterion to avoid the complexity barrier of the optimization problem. Numerical examples show that the proposed distance measures yield solutions far better than the existing ones     

Estimating the number of signals using reference noise samples

Criteria for estimating the number of signals in the case of small-sample populations are considered. The criteria introduced by M. Wax and T. Kailath (1985) do not work well in this case. Two new criteria based on information theoretic and eigenmode composition-based criteria are proposed, assuming a known noise covariance. Simulation results show a significant improvement in the performance of estimating the number of signals in the small-sample case, by the use of the new criteria combined with an estimate of the noise covariance obtained from reference noise samples     

基于语义信息的问题分类

问题分类是问答系统的一个非常基础而重要的子模块。以问题的语义信息为分类依据,建立了一个新的问题分类体系,并提出了一种基于《知网》的自动分类方法。利用该方法对507个问题进行分类实验。实验中问题分类的平均召回率为93.72%,平均正确率为82.23%。实验结果表明,该分类方法是有效的并且以语义信息作为分类依据是可行的。     

Doppler ambiguity resolution using multiple PRF

An algorithm for velocity ambiguity resolution in coherent pulsed Doppler radar using multiple pulse repetition frequencies (PRF) is presented. It relies on the choice of particular values for the PRFs. The folded frequency of the target signal is obtained by averaging the folded frequency estimates for each PRF, and a quasi maximum likelihood criterion is maximized for ambiguity order estimation. The fast implementation of this nonambiguous estimation procedure is based on the fast Fourier transform (FFT), The proposed waveform allows full exploitation of any (even) number of PRFs, which appears to be important for estimation improvement. The effects of the waveform parameters and the folded frequency estimation variance on the performance of the ambiguity order estimation procedure are evaluated theoretically and through computer simulations. Mean square error (MSE) curves are given to assess the Doppler frequency estimation accuracy. Finally, the new method is compared with a classical technique and the implementation of the algorithm in a clutter environment is addressed.     

Multiple model particle flter track-before-detect for range ambiguous radar

The middle pulse repetition frequency（MPRF）and high pulse repetition frequency（HPRF）modes are widely adopted in airborne pulse Doppler（PD）radar systems,which results in the problem that the range measurement of targets is ambiguous.The existing data processing based range ambiguity resolving methods work well on the condition that the signal-to-noise ratio（SNR）is high enough.In this paper,a multiple model particle flter（MMPF）based track-beforedetect（TBD）method is proposed to address the problem of target detection and tracking with range ambiguous radar in low-SNR environment.By introducing a discrete variable that denotes whether a target is present or not and the discrete pulse interval number（PIN）as components of the target state vector,and modeling the incremental variable of the PIN as a three-state Markov chain,the proposed algorithm converts the problem of range ambiguity resolving into a hybrid state fltering problem.At last,the hybrid fltering problem is implemented by a MMPF-based TBD method in the Bayesian framework.Simulation results demonstrate that the proposed Bayesian approach can estimate target state as well as the PIN simultaneously,and succeeds in detecting and tracking weak targets with the range ambiguous radar.Simulation results also show that the performance of the proposed method is superior to that of the multiple hypothesis（MH）method in low-SNR environment.     

Mathematical identification technique and its application in problems of flight vehicle structure design

An approach to the solution of a number of practical important problems of flight vehicle thin-walled structure design based on a unified mathematical identification technique is proposed. These problems include estimation of functional performance of existing structures, development of unconventional promising structures, structure design by a prototype and some others. The original mathematical identification technique contains an identification criterion, identification procedures and methods for formation of structure standards.     

基于决策不确定性的多目标跟踪传感器管理

针对高杂波、电子干扰环境,在量测驱动的多目标滤波框架下提出了一种基于决策不确定性的传感器管理方法。首先,根据部分可观测马尔科夫决策过程的理论,给出了基于Rényi信息增量的传感器管理一般方法。其次,综合考虑决策过程的信息完整性、信息质量、信息的内涵等因素,在量测驱动的自适应滤波框架下,基于目标运动态势评估多目标决策不确定性水平,并选取最大决策不确定性目标。最后,以最大决策不确定性目标的信息增量最大化为准则进行传感器分配方案的求解。仿真实验表明所提方法能够有效抑制电子干扰、杂波对多目标跟踪及传感器分配的影响,与基于威胁的传感器管理方法相比,所提方法的平均最优子模式分配（OSPA）距离及平均计算时长均显著降低,且在高杂波、电子干扰情形下具有较高的可靠性。     

引入稀疏原子特征融合的滑动轴承摩擦故障状态监测

从信息融合理论出发,将特征的稀疏表达作为特征融合参数,提出一种结合K奇异值分解(KSVD）和最大相关最小冗余准则(mRMR）的轴承摩擦故障特征融合算法。该算法采用KSVD对信号稀疏化,将稀疏系数对应的字典原子作为特征融合的参数,用以表达非线性故障信息;针对字典原子集的优化选择问题,基于互信息的mRMR提出一种确定最优原子集的原子数目的准则;最后,通过最大化原则融合稀疏系数,提取故障状态监测的有效信息。轴承摩擦故障模拟实验的结果表明,所提方法能够更好地融合不同特征的故障信息,相比于单特征和其他融合特征方法,提高了约12%的故障识别率。      

Performance improvement for constellation SAR using signal processing techniques

A new concept of spaceborne synthetic aperture radar (SAR) implementation has recently been proposed - the constellation of small spaceborne SAR systems. In this implementation, several formation-flying small satellites cooperate to perform multiple space missions. We investigate the possibility to produce high-resolution wide-area SAR images and fine ground moving-target indicator (GMTI) performance with constellation of small spaceborne SAR systems. In particular, we focus on the problems introduced by this particular SAR system, such as Doppler ambiguities, high sparseness of the satellite array, and array element errors. A space-time adaptive processing (STAP) approach combined with conventional SAR imaging algorithms is proposed which can solve these problems to some extent. The main idea of the approach is to use a STAP-based method to properly overcome the aliasing effect caused by the lower pulse-repetition frequency (PRF) and thereby retrieve the unambiguous azimuth wide (full) spectrum signals from the received echoes. Following this operation, conventional SAR data processing tools can be applied to focus the SAR images fully. The proposed approach can simultaneously achieve both high-resolution SAR mapping of wide ground scenes and GMTI with high efficiency. To obtain array element errors, an array auto-calibration technique is proposed to estimate them based on the angular and Doppler ambiguity analysis of the clutter echo. The optimizing of satellite formations is also analyzed, and a platform velocity/PRF criterion for array configurations is presented. An approach is given to make it possible that almost any given sparse array configuration can satisfy the criterion by slightly adjusting the PRF. Simulated results are presented to verify the effectiveness of the proposed approaches.     

Assessment of some self-adaptive SGS models for wall bounded flows

Self-adaptive subgrid-scale models for large-eddy simulation of wall bounded flows using a second-order accurate numerical method are described and assessed on the plane channel flow configuration. These models do not include any information related to the location of the wall, and are based on the three following approaches: dynamic modelling as proposed by Germano, filtered models and selection function. These three techniques are found to be efficient, i.e. they yield vanishing subgrid viscosity when approaching the solid wall without any information related to its location. Strong sensitivity of the results to both Reynolds number and grid resolution is observed. The selection function is found to lead to the lowest subgrid-viscosity level.     

A multi-frame track-before-detect algorithm based on root label clustering for multiple targets

In this paper, a novel multi-frame track-before-detect algorithm is proposed, which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase. A criterion of track extrapolation is used to construct state transition set, root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space, then measurement plots of multi-frame are divided into sever...     

Blind identification and separation of convolutively mixed independent sources

A trispectra method for solving the m-input n-output (n/spl ges/m) wideband blind identification and signal separation problem with unknown number of sources m is presented. The method is universal in the sense that it does not impose any restriction on the probability distribution of the input signals provided that they are non-Gaussian. A criterion, which states a sufficient condition for identification and separation, has been proved. An algorithm is also developed based on the criterion, whose efficiency is verified by the simulations.     

Novel radar target detection

Herein, a novel method of radar target detection based on 2-dimensional (2-D) fractal dimension is proposed. The proposed approach exploits both range information and azimuth information to estimate fractal dimension. Moreover, the approach can increase the number of data points. The above two merits result in the fractal dimension estimated by this method are more accurate and robust than the previous method. The detection performance is also better than the previous, which only makes use of 1-dimensional (1-D) information to estimate fractal dimension. Theoretical analysis and experimental results show that the proposed method performs well in a strong clutter background. The proposed method is also validated by real-life radar data, and the better result has been achieved.     

Radar target novel characteristic detection method

In this paper, a method of radar target detection based on 2-dimensional (2D) fractal dimension is proposed. The proposed approach exploits both range information and azimuth information to estimate fractal dimension. Moreover, the approach can increase the number of the data points. The above two merits result in the fractal dimension estimated by this method is more accurate and robust than the previous method. The detection performance is also better than the previous one, which only makes use of 1-dimensional (1-D) information to estimate fractal dimension. Theoretical analysis and experimental result show that the proposed method performs well in strong clutter background. The proposed method is also validated by real-life radar data, and the better result has been achieved.     

基于脉组间频率步进的合成超宽带距离像及速度分析

 对脉组间频率步进信号进行信号处理可直接获得合成超宽带(UWB)距离像及目标速度信息,但由于多普勒色散引入的快速傅里叶变换(FFT)输出失配误差和距离多普勒耦合,导致距离像的失真。介绍消除多普勒色散的影响的方法,分析且补偿距离多普勒耦合对距离像的影响,进一步讨论由测速误差产生的补偿量化误差对距离像的影响,并推导由此导致的距离走动公式。提出的迭代二分逼近法使速度分辨提高 N 倍( N 为脉组个数),从而得到目标真实高分辨距离像。仿真实验结果表明多普勒色散得到消除且 N 的选取更加灵活。     

The Modified Yule-Walker Method of ARMA Spectral Estimation

An overview of ARMA spectral estimation techniques based on the modified Yule-Walker equations is presented. The importance of using order overestimation, as well as of using an overdetermined set of equations, is emphasized. The Akaike information criterion is proposed for determining the equation order. A procedure for removing spurious noise modes based on modal decomposition of the sample covariance matrix is derived. The role of the singular value decomposition method in solving the modified Yule-Walker equations is discussed. A number of techniques for estimating MA spectral parameters are presented.     

基于模糊数的航空发动机可靠性预计模型

根据模糊数适于量化模糊信息的特点,运用模糊集和区间分析理论,提出了一种基于模糊数的多级模糊综合评判方法,它能更好地根据模糊信息作出模糊决策。在此基础上,结合航空发动机可靠性的特点,建立了一种航空发动机可靠性预计的模型。该模型的特点是能够在综合考虑各种可靠性影响因素及其模糊性的情况下,对发动机的可靠性水平作出较为客观的综合评判,尤其适用于航空发动机设计初期的可靠性预计。      

Observability in passive target motion analysis

The nature of observability of nonlinear systems encountered in passive target motion analysis (TMA) is carefully examined. The approach proposed here is based upon a well-chosen criterion which allows us to answer the major observability questions