Multi-target localization for bistatic MIMO radar in the presence of unknown mutual coupling

A decoupling-estimation signal parameters via rotarional invariance technique(ESPRIT) method is presented for multi-target localization with unknown mutual coupling in bistatic multiple-input multiple-output(MIMO) radar.Two steps are carried out in this method.The decoupling operation between angle and mutual coupling estimates is realized by choosing the auxiliary elements on both sides of the transmit and receive uniform linear arrays(ULAs).Then the ESPRIT method is resilient against the unknown mutual coupling matrix(MCM) and can be directly utilized to estimate the direction of departure(DOD) and the direction of arrival(DOA).Moreover,the mutual coupling coefficient is estimated by finding the solution of the linear constrained optimization problem.The proposed method allows an efficient DOD and DOA estimates with automatic pairing.Simulation results are presented to verify the effectiveness of the proposed method.     

基于多波位杂波的通道相位误差估计方法

雷达的通道误差会影响杂波抑制效果和参数估计性能。为解决此问题,提出了一种针对机载扫描模式雷达的通道相位误差估计方法。首先对不同接收通道的杂波数据进行干涉处理,然后结合扫描角来求解通道相位误差。为了减小系统噪声影响,提高估计精度,选取主杂波内独立同分布的距离单元作为估计样本。另外,对多个波位的杂波求平均进一步降低了估计误差。机载雷达实测数据处理结果表明,该方法可以较好地补偿通道相位误差,并且可以显著提高系统的目标定位能力。     

稳健二进前向网络的遗传训练方法

稳健二进前向网络是对逻辑知识的隐式表示和显式表示的有机统一,是性能优良的逻辑知识库、推理机和解释器,但目前还没有一种网络训练算法能够训练出稳健二进前向网络,针对这种情况,本文首先对稳健二进前向网络的神经元辅阈值范围进行了改进和有效描述,并在此基础上提出了一种对稳健二进前向网络进行有效训练的遗传训练方法。在这种训练方法中,网络参数采用三值编码方案,并运用相应的变异机制和有效的适应度函数,经这种方法训练出的网络具有结构最优、稳健性能最强和最易实现的特点。文章最后给出了实验的结果。     

证券投资决策的微分对策方法研究

在证券价格存在有界不确定性的假设下,研究了基于最差情况的最优证券投资决策问题。首先建立了证券投资决策的微分对策模型,然后,证明了该微分对策模型存在唯一的值函数,最后,根据微分对策理论得出了值函数所满足的偏微分方程。     

曲面薄壳有限元离散误差分析和修正方法研究

基于曲面薄壳几何/物理方程和有限元离散原理推导,揭示了壳单元离散方法特点、刚度矩阵误差产生机理和范数表示方法。以航空涡轮发动机测压探针为例,深入研究了利用协调平板壳单元、非协调平板壳单元、一次和二次实体退化壳单元等不同类型单元离散曲面薄壳时,应力计算误差影响因素及变化规律,综述了单元的适用条件。基于Boltzmann模型提出了应力计算结果修正通式,通过FORTRAN语言编程修正应力仿真结果和ANSYS二次开发语言APDL命令实现应力修正结果的读取和后处理,应力修正结果与理论解相对误差小于5%,满足曲面薄壳设计和稳定性分析应用要求。

Abstract：

Based on the geometric physical equations and deduction of discrete process of finite element for curved thin-walled structure,characteristics of shell elements for discretion and discrete error for element stiffness matrix were analyzed theoretically,and the norm expression of discrete error for element stiffness matrix was put forward.Then,as an illustration,a probe for detecting the pressure inside turboprops was meshed by curved thin-walled shell element,compatible flat shell element,incompatible flat shell element and 3D degenerated shell element,respectively,and the stress of the probe was numerically calculated by FEM.On the basis of solved results,the stress error was analyzed thoroughly,the influence factors on the error and the applying situations for different elements were determined.Finally,the general form of correctional function was put forward according to the Boltzmann model.The program developed with FORTRAN was used to correct the numerical results and the APDL commands inside ANSYS software were utilized to read and plot the correctional stress results.Through the correction,the discrete error can be reduced within 5% that satisfies the requirement of application for structural design and stability analysis.     

求解约束优化问题的动量粒子群算法

为解决约束优化问题,提出使用双可行域吸引子策略改进动量粒子群算法。该算法只需初始种群中有一个粒子位于可行域内,随着搜索过程的进行,整个种群自动进入可行域内搜索。一方面,在搜索过程早期,由于可行域内粒子少,所有粒子移向相同的吸引子,整个种群迅速进入可行域内。另一方面,随着进入可行域粒子的增多,由于每个粒子使用距本身最近的可行域吸引子,较好地维持了种种群的多样性,避免早熟现象的发生,使算法具有较好的寻优性能。与国际上当前解决约束优化问题的粒子群算法在4个标准约束优化函数上测试比较,实验结果表明本算法取得的最优值要优于其它粒子群算法。

Abstract：

The strategy that two good positions in feasible region worked as attractors was incorporated into momentum particle swarm optimization algorithm in order to resolve constrained optimization problems. The resulting algorithm only requires that one of the initial particles is in the feasible region, and then all particles in the swam automatically move into the feasible region. On the one hand, in the early iterations few particles appear in the feasible region and hence all particles move toward the same attractors, so the particles soon enter into the feasible region. On the other hand, as the number of particles in the feasible region increases, each particle adopts the most near attractor so that each particle has different attractor. Therefore, the algorithm maintains the diversity of the population, alleviates the premature, and hence achieves good performance. The algorithm is compared with other particle swarm optimization algorithms on four benchmark functions. The experimental results show that the solution of the algorithm is better than that of others.     

Coordinated Bayesian optimal approach for the integrated decision between electronic countermeasure and firepower attack

The coordinated Bayesian optimization algorithm (CBOA) is proposed according to the characteristics of the function independence, conformity and supplementary between the electronic countermeasure (ECM) and the firepower attack systems. The selection criteria are combinations of probabilities of individual fitness and coordinated degree and can select choiceness individual to construct Bayesian network that manifest population evolution by producing the new chromosome. Thus the CBOA cannot only guarantee the effective pattern coordinated decision-making mechanism between the populations, but also maintain the population multiplicity, and enhance the algorithm performance. The simulation result confirms the algorithm validity.     

含有铁磁迟滞非线性系统的反步控制及仿真

铁磁迟滞特性反映的是磁性材料中的迟滞作用.研究一类含有铁磁迟滞特性的不确定非线性系统的跟踪控制问题.将铁磁迟滞模型近似处理成线性输入项和有界的非线性扰动项,使得控制器设计易于实现.将迟滞模型融入控制器的设计过程中有效地消除了迟滞作用对系统的影响,避免了构造复杂迟滞逆模型需要精确迟滞模型表达式的限制.采用自适应反步控制方法,所设计的控制器使系统的输出快速跟踪上给定信号,跟踪误差在一个很小的范围内波动,保证了闭环系统的全局稳定.仿真研究验证了该设计方法的有效性.     

具有时变扰动不确定系统的对偶自适应控制

对一类具有参数不确定性以及时变外部输入扰动的线性系统的控制方法进行了研究 :(1)提出了一种系统参数和扰动的联合辨识实时辨识方法 ,即在不需预先知道扰动模型的情况下 ,同时估计被控对象参数与外部输入扰动 ,并根据辨识结果对输入扰动进行实时补偿 ;(2 )设计了一对偶自适应控制器 ,该控制器具有控制与学习的双重作用 ,能够克服系统参数不确定性影响 ,且防止了传统自适应系统可能有的“关断” ,“终止”和“猝发”等不良现象 ,从而增强了自适应控制系统的鲁棒性。最后通过仿真例子验证了算法的有效性。     

构造极小广播网络的新方法

广播是指从网络中某一成员开始 ,将消息传递给网络中其余成员的过程 .极小广播网络就是指能在最短时间内广播一条消息的通讯网络 .本文主要讨论一种构造极小广播网络的新方法 ,这种方法在大多数情况下改进了极小广播网络的最少边数的上界.