《信号与系统》课程教学改革探讨

《信号与系统》是高等院校电子、通信类专业开设的一门重要的专业基础课.在教学改革中,应优化教学内容、改革教学方法、完善考核措施,尤其是加强实践教学,培养学生的实际工程能力.只有通过教改实践,才能调动学生学习的积极性,提高教学质量.     

多传感器量测自适应Rao-Blackwellised粒子滤波算法

针对量测不确定下非线性系统状态估计中多传感器量测数据的有效利用和计算复杂度的简化问题,给出了一种多传感器量测自适应Rao-Blackwellised粒子滤波算法。首先,通过随机采样策略和量测模型先验转移概率实现用于评估粒子权重的传感器有效量测集合的采样;其次,利用重采样步骤和概率最大化原则完成对不含扰动影响传感器量测模型的辨识;最终,依据Rao-Blackwellised粒子滤波中非线性状态分量和线性状态分量的独立求解方式实现当前时刻系统的状态估计。理论分析和仿真实验结果验证了算法的可行性和有效性。     

基于Einstein算子的证据冲突度量方法

为了有效度量融合证据之间的冲突,在分析冲突因子和典型证据冲突度量方法不足的基础上,提出了一种基于Einstein算子的证据冲突度量方法。首先,利用证据向量度量思想给出证据之间的差异度矩阵,并定义对数形式的差异因子;然后,引入模糊集相似关系定义证据之间的相关系数;最后,综合考虑证据之间的差异性和相关性,利用Einstein算子定义一种新的证据冲突度量因子。仿真实验结果表明:该方法不但可以有效度量证据之间的冲突程度,而且对冲突证据的融合具有良好的收敛性。     

《信号与系统》课程教学改革与探索

信号与系统是高等学校本科电子、通信类专业开设的一门重要专业基础课,在信息科学与技术领域有着突出的作用。针对该课程特点,并结合河南大学教学实际情况,从教学内容的优化、教学方法和手段、实践教学和考试方法和内容等5个方面探讨了对本课程教学的改革与创新探索,通过改革调动了学生学习积极性,提高了教学质量。     

大学生创新素质培养的探索与研究

对当前高等学校教育中大学生创新素质的现状进行了简要分析，归纳了影响大学生创新素质培养的要素。在此基础上提出了涉及：大学精神、个性培养、德育、评价机制等方面的当代大学生创新素质培养的探索与研究方案。     

A novel maneuvering multi-target tracking algorithm based on multiple model particle filter in clutters

To solve the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutter environment, a novel maneuvering multi-target tracking algorithm based on multiple model particle filter is presented in this paper. The algorithm realizes dynamic combination of multiple model particle filter and joint probabilistic data association algorithm. The rapid expan- sion of computational complexity, caused by the simple combination of the interacting multiple model algorithm and particle filter is solved by introducing model information into the sampling process of particle state, and the effective validation and utilization of echo is accomplished by the joint proba- bilistic data association algorithm. The concrete steps of the algorithm are given, and the theory a- nalysis and simulation results show the validity of the method.     

基于相对距离的一种多传感器数据融合方法

分析了多传感器量测数据不确定性产生的原因,提出了一种基于相对距离的多传感器数据融合方法。该方法利用模糊集合理论中隶属度函数的优点,通过挖掘量测数据间冗余和互补信息调整数据间的相互支持程度,进而合理地分配量测数据在融合过程中所占权重。同时,避免了关联门限的设定,从而降低了先验信息的影响,避免了量测数据间相互支持的绝对化。通过仿真实例,验证了该方法的有效性。     

矢量传感器声源方位与噪声协方差阵联合估计

针对空域非均匀白噪声,通过矩阵变换估计噪声协方差矩阵,进而采用噪声预白化技术获取声源方位的最大似然估计,该方法避免了原最大似然算法加权参数选取的一维搜索过程,提高了算法效率;针对空域色噪声,利用噪声协方差矩阵的先验分布信息,结合噪声预白化技术,提出声源方位与噪声协方差矩阵迭代联合估计算法.仿真实验表明,空域非均匀白噪声背景下,与原最大似然估计方法相比,方位估计精度基本相同,但算法效率更高;空域色噪声背景下,与原最大似然估计方法相比,该方法提高了方位估计精度.     

Federal extended Kalman filter based on reconstructed observation in incomplete observations

In the estimation and identification of nonlinear system state,aiming at the adverse effect of observation missing randomly caused by detection probability of used sensor which is less than 1,a novel federal extended Kalman filter( FEKF) based on reconstructed observation in incomplete observations( ROIO) is proposed in this paper. On the basis of multi-sensor observation sets,the observation is exchanged at different times to construct a new observation set. Based on each observation set,an extended Kalman filter algorithm is used to estimate the state of the target,and then the federal filtering algorithm is used to solve the state estimation based on the multi-sensor observation data. The effect of the sensor probing probability on the filtering result and the effect of the number of sensors on the filtering result are obtained by the simulation experiment,respectively. The simulation results demonstrate effectiveness of the proposed algorithm.     

A novel multi-sensor multiple model particle filter with correlated noises for maneuvering target tracking

Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.