Stochastic structural optimization using particle swarm optimization, surrogate models and Bayesian statistics

This paper focuses on a method to solve structural optimization problems using particle swarm optimization (PSO), surrogate models and Bayesian statistics. PSO is a random/stochastic search algorithm designed to find the global optimum. However, PSO needs many evaluations compared to gradient-based optimization. This means PSO increases the analysis costs of structural optimization. One of the methods to reduce computing costs in stochastic optimization is to use approximation techniques. In this work, surrogate models are used, including the response surface method (RSM) and Kriging. When surrogate models are used, there are some errors between exact values and approximated values. These errors decrease the reliability of the optimum values and discard the realistic approximation of using surrogate models. In this paper, Bayesian statistics is used to obtain more reliable results. To verify and confirm the efficiency of the proposed method using surrogate models and Bayesian statistics for stochastic structural optimization, two numerical examples are optimized, and the optimization of a hub sleeve is demonstrated as a practical problem.     

Detection probabilities for correlated Rayleigh fading signals

Both the method of saddlepoint integration and its associated saddlepoint approximation are applied to calculating the probability of detecting correlated Rayleigh-fading signals in Gaussian noise by means of a detector that integrates M samples of the output of a quadratic rectifier. The quadrature components of the signal samples are modeled as an autoregressive moving-average process, and specific results are exhibited for a first-order Markov process. By these methods the fluctuation loss can be computed for much larger values of M and for larger values of the detection probability than previously. Values calculated by the saddlepoint approximation prove to be close enough to the exact values to be useful over a broad range of signal parameters     

Exact Formulation of Window Functions for Analog Network Spectral Analyzers

An equation for the spectral window function for analog network analyzers is derived without assumptions on length of data interval available. The equation, given in terms of filter pole-zero locations for both the filter-bank and heterodyned-input type analyzers, can be programmed on a digital computer without difficulty. As an illustration, the "growth" of a spectral window with increasing length of available data is shown for a special case: a single-tuned circuit followed by a squaring circuit and perfect integrator. The error in using the usual approximation also is indicated. Use of the derived equation for quantitative comparisons of analog and digital analyzers is noted.     

Statistical characterization of clutter scenes based on a Markov random field model

The problem of clutter region identification based on Markov random field (MRF) models is addressed. Observations inside each clutter region are assumed homogenous, i.e., observations follow a single probability distribution. Our goal is to partition clutter scenes into homogenous regions and to determine this underlying probability distribution. Metropolis-Hasting and reversible jump Markov chain (RJMC) algorithms are used to search for solutions based on the maximum a posteriori (MAP) criterion. Several examples illustrate the performance of our algorithm.     

Bayesian gamma mixture model approach to radar target recognition

This paper develops a Bayesian gamma mixture model approach to automatic target recognition (ATR). The specific problem considered is the classification of radar range profiles (RRPs) of military ships. However, the approach developed is relevant to the generic discrimination problem. We model the radar returns (data measurements) from each target as a gamma mixture distribution. Several different motivations for the use of mixture models are put forward, with gamma components being chosen through a physical consideration of radar returns. Bayesian formalism is adopted and we obtain posterior distributions for the parameters of our mixture models. The distributions obtained are too complicated for direct analytical use in a classifier, so Markov chain Monte Carlo (MCMC) techniques are used to provide samples from the distributions. The classification results on the ship data compare favorably with those obtained from two previously published techniques, namely a self-organizing map and a maximum likelihood gamma mixture model classifier.     

3种气动弹性状态空间建模方法的对比

研究气动弹性状态空间建模的3种常用方法:最小二乘(LS)法、最小状态(MS)法和拟合状态空间(MA)法.用2个算例从颤振和频响特性角度分析和总结了它们的建模特点.在气动力有理函数拟合建模方法(LS法、MS法)研究中,着重分析滞后根的影响;在MA法的研究中,讨论了建模的特点.最后,系统对比了3种建模方法的建模效果及使用原则,为这些方法的工程应用提供参考.仿真计算结果表明,MS法建立的模型阶数低、精度适中且使用方便,是比较好的方法,而MA法建立的模型频响特性与参考结果最为接近.     

地磁测量数据野值的辨识与剔除

针对地磁测量野值的辨识与剔除方法进行了深入研究。分析了野值辨识与剔除的基本原理,基于孤立型野值和斑点型野值模型分析比较了不同的野值剔除方法。利用地磁场实测信息,优化了野值剔除方法的参数,并从不同角度验证了方法的野值修复效果。仿真结果表明,在观测信息为总强度地磁信息条件下,优化的最小二乘B样条逼近法能够有效地辨识野值,具有较好的野值修复效果。     

密集杂波背景下的水下多目标跟踪方法

水下多目标跟踪是水声信号处理领域研究的热点和难点问题。高斯混合概率假设密度（Gaussian mixture probability hypothesis density, GM-PHD）滤波器以其高效的计算效率为解决水下多目标跟踪问题提供了保证。然而，GM-PHD滤波器在跟踪目标时需要先验已知新生目标的强度，否则其性能会出现严重退化。针对该问题，提出一种滑动窗两步初始化高斯混合概率假设密度（sliding window two step initialization GM-PHD, SWTSI-GMPHD）滤波器。将提出的滑动窗两步初始化方法嵌入GM-PHD滤波器，利用滑动窗两步初始化方法估计新生目标强度，减少杂波干扰导致跟踪结果中出现的虚假目标。仿真实验表明，在杂波密集环境下，相较于其他跟踪方法，提出方法将跟踪精度提高69.84%，52.62%和41.05%。     

Attitude Determination Via Nonlinear Filtering

The theory of nonlinear filtering has been applied to the problem of the attitude determination of a satellite. Two approximation methods have been studied with a view to practical implementation. Computer simulation results are included.     

马尔可夫分析在系统安全性评估中的应用研究

安全性分析是适航符合性验证的重要方法。对马尔可夫分析在系统安全性评估中的应用进行了深入研究,阐明了马尔可夫分析的作用与应用时机,结合功能危险状态“V1速度后起落架无指令收起”分别给出了在初步系统安全性评估与系统安全性评估中进行马尔可夫分析的一般方法,将马尔可夫分析与故障树分析的结论进行了对比,袁明了本文方法的适用性。     

相控阵雷达长时跟踪波束调度与波形优化策略

针对相控阵雷达多目标跟踪波束调度和波形参数优化控制的问题，本文提出了一种基于马尔可夫决策过程（MDP）的相控阵雷达跟踪波束调度与波形参数优化策略，该方法以无迹卡尔曼滤波（UKF）算法为基础来估计目标的状态。首先将本文的序列决策问题建模为马尔可夫决策过程，定义了资源的效费比和长期回报率，然后与当前实际跟踪误差综合考虑作为MDP的回报函数，进而给出了调度的优化模型，最后将长时决策问题转化为动态规划算法结构进行求解，并且提出了一种并行混合遗传粒子群优化算法来求解各决策时刻的最优策略。仿真结果表明了长时策略的先进性以及寻优算法的优越性，与传统的短时策略相比，跟踪精度可提高11.17%。     

Computer Simulation of Synthetic Aperture Radar Images of Three-Dimensional Objects

A digital simulation of coherent synthetic aperture radar (SAR) images of three-dimensional objects is described. The simulation is intended to produce representative SAR images that would be suitable for image analysis and pattern recognition studies. The procedure involves a modeling of the object using a combination of three-dimensional quadratic shapes yielding a smooth surface representation. The radar images of these models are then computed using physical optics scattering theory. Finite resolution both in range and cross-range direction is incorporated via a theoretical analysis which results in a simple Fourier transform representation of an equivalent "offset" window filter. Examples of the computer simulation for both infinite resolution and blurred or finite resolution are given for a KC-135 aircraft model.     

面向空中目标威胁评估的多传感器管理方法

为了降低在空中目标威胁评估任务中由于威胁评估结果的不准确性和传感器辐射所带来的潜在损失，提出了一种基于风险的多传感器管理方法。首先，基于部分可观马尔可夫决策过程建立了传感器管理模型；然后，给出了基于信息状态的威胁评估风险和传感器辐射风险的预测方法以量化潜在损失；接着，为获得更优的作战收益，以多步风险预测值为决策依据，以两种风险的加权和最小为优化目标建立了长期目标函数；最后，在求解目标函数时，将传感器管理问题转化为决策树搜索，设计了一种基于分支定界的标准代价搜索算法以快速获得高质量的管理方案。仿真实验表明，所提算法能够在搜索到高质量解的同时大幅减少计算时间和内存消耗；所提方法能够对风险进行准确预测，且相比于经典的传感器管理方法，所提方法具有更好的风险控制效果。     

战略储备系统备件最优储备量计算的解析方法

 首先简要介绍了战略储备系统及其寿命过程。然后从分析系统的寿命过程入手,将其寿命过程分为两个阶段,每个阶段分别用一连续时间马尔可夫链来描述系统的动态过程,在此基础上给出了战略储备系统最优战略储备量计算的解析方法,并针对不可修的情况给出了简化的计算公式。     

换热反问题输入数据的正则预处理及其对解的影响

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Frequency line tracking using HMM-based schemes [passive sonar]

This paper deals with fluctuating line tracking, present on the so-called lofargram (low frequency analysis and recording) encountered in any passive sonar system. Considering such a line as a random walk modeled by a first-order Markov chain, we have recourse to the hidden Markov models (HMMs) arsenal. More precisely, we propose to track a frequency line with Viterbi and forward-backward algorithms. The originality of this work comes from the fact that a "probabilistic integration of the spectral power" approach allows us to construct a signal-to-noise (SNR)-knowledge-free method. Intensive simulations reveal no loss of performance.     

Pop-up threat models for persistent area denial

Pop-up threats usually appear or disappear randomly in a battle field. If the next pop-up threat locations could be predicted it would assist a search or attack team, such as in a persistent area denial (PAD) mission, in getting a team of unmanned air vehicles (UAVs) to the threats sooner. We present a Markov model for predicting pop-up ground threats in military operations. We first introduce a general Markov chain of order n to capture the dependence of the appearance of pop-up threats at previous locations of the pop-up threats over time. We then present an adaptive approach to estimate the stationary transition probabilities of the nth order Markov models. To choose the order of the Markov chain model for a specific application, we suggest using hypothesis tests from statistical inference on historical data of pop-up threat locations. Anticipating intelligent responses from an adversary, which might change its pop-up threat deployment strategy upon observing UAV movements, we present adaptive Markov chain models using a moving horizon approach to estimate possibly abrupt changes in transition probabilities. We combine predicted and actual pop-up target locations to develop efficient cooperative strategies for networked UAVs. A theoretical analysis and simulation results are presented to evaluate the Markov model used for predicting pop-up threats. These results demonstrate the effectiveness of cooperative strategies using the combined information of threats and predicted threats in improving overall mission performance.     

软件测试的控制论方法

 软件测试被认为是软件开发过程中理解最为不清的一环。部分原因是虽然有很多策略被定义和分析,但很少有策略被设计和优化。软件测试的反馈机制迄今尚未形成。基于此种情况,软件测试的控制论方法将软件测试问题当作控制问题,被测软件当作被控对象,软件测试策略当作相应的控制器,被测软件和测试策略构成一个闭环反馈控制系统。软件控制论是一门探讨软件理论和工程与控制理论和工程交叉的学科,软件测试的受控马尔可夫链方法利用受控马尔可夫链理论设计和优化软件测试策略,是软件控制论思想的体现。在受控马尔可夫链方法的框架内讨论软件系统的自适应测试,并与随机测试进行比较,发现自适应测试方法相对于传统的随机测试方法具有较大的优越性。     

A feedback-retransmission based asynchronous frequency hopping MAC protocol for military aeronautical ad hoc networks

Attacking time-sensitive targets has rigid demands for the timeliness and reliability of information transmission, while typical Media Access Control (MAC) designed for this application works well only in very light-load scenarios; as a consequence, the performances of system throughput and channel utilization are degraded. For this problem, a feedback-retransmission based asynchronous FRequency hopping Media Access (FRMA) control protocol is proposed. Burst communication, asynchronous Frequency Hopping (FH), channel coding, and feedback retransmission are utilized in FRMA. With the mechanism of asynchronous FH, immediate packet transmission and multi-packet reception can be realized, and thus the timeliness is improved. Furthermore, reliability can be achieved via channel coding and feedback retransmission. With theories of queuing theory, Markov model, packets collision model, and discrete Laplace transformation, the formulas of packet success probability, system throughput, average packet end-to-end delay, and delay distribution are obtained. The approximation accuracy of theoretical derivation is verified by experimental results. Within a light-load network, the proposed FRMA has the ability of millisecond delay and 99% reliability as well as outperforms the non-feedback-retransmission based asynchronous frequency hopping media access control protocol.     

基于马尔可夫方法的飞控系统安全性评估

安全性评估是适航符合性验证的重要方法。对马尔可夫分析在系统安全性评估中的应用进行了深入研究．给出了马尔可夫在系统安全性评估中的一般分析方法。运用马尔可夫方法和故障树方法分别对飞行控制系统的同一种失效状态进行了建模分析．并对二者的分析结果进行了比较,得出对并联顺序系统进行安全性分析时马尔可夫分析方法具有更高的定量分析精度,并克服了故障树静态分析特性的缺点。