EXTENDED SELF SIMILARITY OF PASSIVE SCALAR IN RAYLEIGH-BENARD CONVECTION FLOW BASED ON WAVELET TRANSFORM

Wavelet transform is used to analyze the scaling rule convection flow from two aspects. By utilizing the method of extended self similarity (ESS), one can find the obtained scaling exponent agrees well with the one obtained from the temperature data in a experiment of wind tunnel. And then we propose a newly defined formula based on wavelet transform, and can determine the scaling exponent ξ(q) of temperature data. The obtained results demonstrate that we can correctly extract ξ(q) by using the method which is named as wavelet transform maximum modulus (WTMM).``     

一种基于计算几何控制无量纲参数的叶片造型方法

应用目前先进的自由曲线理论,开发了一种新的叶片造型方法——B样条控制中线角叶型(BMAA)方法。分别选取准均匀B样条和贝塞尔曲线,实现了对叶型中线和厚度的方便、灵活控制;同时综合考虑方便性与实用性,选取四点控制参数进行设计。分别利用BMAA方法与原有定制叶型、任意中线方法,对某单级压气机进行设计,结果表明:对于超/跨声叶片设计,BMAA方法设计的叶片性能略优于定制叶型;与任意中线叶型达到的性能相当,但叶型控制便捷性和叶型光滑性优于任意中线叶型。     

一种针对恒模信号的运动单站直接定位算法

相比于常规的"测向+位置估计"两步定位模式,以Weiss等提出的目标直接位置确定(DPD)算法具有估计精度高、分辨能力强和无需数据关联等诸多优点。基于该类定位算法的基本理念,提出了一种利用单个运动天线阵列对恒模(即相位调制)信号的DPD算法。首先,依据最大似然(ML)准则以及恒模信号的恒包络特征,建立了相应的直接定位优化模型;接着,根据优化函数的代数特征提出了一种有效的多参量交替迭代算法,用以获得ML估计器的最优数值解;此外,推导了针对恒模信源的位置直接估计方差的克拉美罗界(CRB),从而为新算法的定位精度提供定量的理论下界。仿真实验表明:相比于已有的基于单个运动天线阵列的直接定位算法以及传统的两步定位算法,通过利用恒模信号的恒包络特征可以明显提高目标直接定位的估计精度。     

一种基于莱维飞行的新型改进平衡全局优化算法

本文将莱维飞行（Lévy flight）和平衡优化算法（EO）相结合，提出了一种新型的全局优化算法（LEO）。该算法从平衡优化算法核心出发，采用莱维飞行轨迹实现了在探索和开发之间更好的平衡，并且可以有效防止算法陷入局部最优解。同时给出了新型全局优化算法的数学模型和算法流程，进一步保证了算法中全局搜索和局部开发之间平衡性。将该算法应用于基准测试函数求解以测试算法性能，通过与主流智能算法对比，优化结果表明新算法在解决优化问题方面表现更为优越，为更多工程问题的解决提供了新的思路。     

A Linear-correction Least-squares Approach for Geolocation Using FDOA Measurements Only

  A linear-correction least-squares(LCLS) estimation procedure is proposed for geolocation using frequency difference of arrival (FDOA) measurements only. We first analyze the measurements of FDOA, and further derive the Cram閞-Rao lower bound (CRLB) of geolocation using FDOA measurements. For the localization model is a nonlinear least squares(LS) estimator with a nonlinear constrained, a linearizing method is used to convert the model to a linear least squares estimator with a nonlinear constrained. The Gauss-Newton iteration method is developed to conquer the source localization problem. From the analysis of solving Lagrange multiplier, the algorithm is a generalization of linear-correction least squares estimation procedure under the condition of geolocation using FDOA measurements only. The algorithm is compared with common least squares estimation. Comparisons of their estimation accuracy and the CRLB are made, and the proposed method attains the CRLB. Simulation results are included to corroborate the theoretical development.     

基于Cramér-Rao下限的多传感器跟踪资源协同分配

针对防空指挥控制系统中多传感器管理问题,提出了一种基于目标跟踪精度Cramér-Rao下限的多传感器跟踪资源协同分配方法。该方法首先利用目标战术重要性函数求解目标优先级;然后在目标优先级函数和目标—传感器配对效能函数的基础上,构造了多传感器资源协同分配一般模型,并根据目标跟踪过程特点将Cramér-Rao下限引入到协同分配的模型中,使得在进行跟踪资源协同分配时无需考虑目标跟踪滤波算法的选择。对于分配过程中出现的NP(Non-deterministic polynomial)难问题,探讨了利用匈牙利算法寻求满足条件的目标传感器最优组合,给出了模型求解的步骤。仿真结果表明,这种多传感器跟踪资源协同分配方法的可行性与模型求解的快速性。