Bayesian inference for a Lanchester type combat model

We undertake inference for a stochastic form of the Lanchester combat model. In particular, given battle data, we assess the type of battle that occurred and whether or not it makes any difference to the number of casualties if an army is attacking or defending. Our approach is Bayesian and we use modern computational techniques to fit the model. We illustrate our method using data from the Ardennes campaign. We compare our results with previous analyses of these data by Bracken and Fricker. Our conclusions are somewhat different to those of Bracken. Where he suggests that a linear law is appropriate, we show that the logarithmic or linear‐logarithmic laws fit better. We note however that the basic Lanchester modeling assumptions do not hold for the Ardennes data. Using Fricker's modified data, we show that although his “super‐logarithmic” law fits best, the linear, linear‐logarithmic, and logarithmic laws cannot be ruled out. We suggest that Bayesian methods can be used to make inference for battles in progress. We point out a number of advantages: Prior information from experts or previous battles can be incorporated; predictions of future casualties are easily made; more complex models can be analysed using stochastic simulation techniques. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 541–558, 2000     

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

Quantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study.     

在线学习的主用户仿冒攻击策略

在认知无线网络中,次用户通过频谱感知来学习频谱环境,从而接入那些没有被主用户占用的频谱空隙。事实上,多种恶意攻击的存在会影响次用户频谱感知的可靠性。只有深入研究恶意攻击策略,才能确保认知无线网络的安全。基于此,研究了一种认知无线网络中的欺骗性干扰策略,即主用户仿冒攻击策略,该攻击策略通过在信道上传输伪造的主用户信号来降低次用户频谱感知的性能。具体来说,将攻击策略问题建模为在线学习问题,并提出基于汤普森采样的攻击策略以实现在探索不确定信道和利用高性能信道间的权衡。仿真结果表明,与现有的攻击策略相比,提出的攻击策略能更好地通过在线学习优化攻击决策以适应非平稳的认知无线网络。     

相参雷达间歇采样灵巧干扰方法

间歇采样转发干扰是针对相参雷达一种有效的干扰方法,它利用脉压雷达匹配滤波特性,能够对雷达形成假目标干扰.首先分析了间歇采样转发干扰方法,指出了间歇采样转发干扰的不足之处,即脉压后干扰能量损失和假目标分布均匀,进而提出了间歇采样灵巧干扰方法,新方法形成的假目标分布不均匀,使得雷达识别真假目标可能性大大降低,仿真实验验证了新干扰方法的有效性.     

尺度自适应特征压缩跟踪

为在复杂环境中对目标进行长时间精确跟踪,提出一种尺度自适应特征压缩跟踪算法。通过结构约束性采样,获取不同尺度不同位置的扫描窗,离线计算不同尺度下的稀疏随机感知矩阵。在线跟踪时利用这些矩阵感知对应尺度的图像采样块,实现特征降维,提高运算速度。利用朴素贝叶斯分类器对降维特征判决,在线学习更新分类器参数,找出具有最高分类得分的采样块作为新的跟踪结果,实现跟踪位置及尺度的自适应更新。实验结果表明,该算法能适应目标的基本姿态变化及尺度缩放,不依赖于目标初始跟踪区域尺度选取,跟踪结果具有较强的鲁棒性。     

捷变频雷达信号频域侦察截获技术研究

现在的军事雷达大多数采用捷变频技术,这给反辐射导引头侦察雷达信号带来了困难。针对反辐射导引头对均匀分布的雷达捷变频信号的窄带搜索问题,提出均匀分布和高斯分布2种不同于常规的侦察方式模型,并给出在2种侦察模型下性能评估模型;通过实例仿真,验证了模型的正确性。     

均匀面积映射及其在均匀纹理映射中的应用

为形成一个具有更小拉伸的网格参数化映射,在三角形网格面片面积均匀分布的情况下,采用分两步走的均匀面积映射法。通过均质化参数域里的三角形面积,用均匀或不均匀方式有效地降低了采样稀疏问题的影响,避免了传统方法中矩阵奇异值的计算。对三角形网格,先进行均值协调参数化,然后通过一个凸组合让参数域更加均匀,得到一个拉伸较低、面积更加均匀的三角形分布,从而可以更好地取样且适合于纹理映射。通过实验和对比,可以证明在均匀纹理映射方面,均匀面积映射法比保角映射法和等积投影映射法都有明显的提高。     

优化拉丁方试验设计方法及其应用

计算机仿真是复杂系统优化的一种有效手段,但需要耗费大量机时,必须严格限制仿真次数。针对此提出了优化拉丁方试验设计方法,该方法需要较少的仿真次数,且兼顾方案的正交性和均匀性,采用Cholesky分解生成初始解,通过模拟退火算法对拉丁方矩阵进行优化,定义动态权重因子实现正交性与均匀性的权衡。最后构建了一个实例,通过试验结果证明采用优化拉丁方试验设计方法可以生成具有较好性质的仿真方案,且仿真次数少。     

基于磁记忆的铁磁性构件定量无损检测研究

为提高磁记忆检测结果的稳定性,设计匀速采样系统,并且针对系统的实验结果进行分析。由实验结果得出提离值对系统稳定性的影响较小,扫描速度对检测结果稳定性的影响较大。     

高炮防空反导系统有关环节采样频率的分析与优选

从系统的精度、平稳性和硬件造价、计算机负担等方面对高炮防空反导系统中的目标测定器输出采样频率和棱镜伺服系统采样频率的取值进行了理论分析和计算机仿真,在此基础上给出了最佳取值范围。