信号直接调制色泵噪声的单模激光光强涨落

应用线性化近似方法计算了信号直接调制方式下由具有色关联的色泵噪声和色量子噪声驱动的单模激光增益模型的光强相对涨落，分析了噪声和调制信号等参量对光强相对涨落的影响，结果表明，泵噪声自相关时间与量子噪声自相关时间及其比值对光强相对涨落起到重要的作用．     

负关联噪声驱动双稳系统的随机动力学

应用具有负关联噪声驱动系统的一般福克－普朗克方程，研究了具有负关联的加法和乘法高斯白噪声驱动的双稳系统，导得了该情形下的福克－普朗克方程．比较了双稳系统在正关联和负关联的高斯白噪声驱动下，定态几率分布和极值位置的变化，以及有关的各参数对定态几率分布单双峰转变的影响．从中发现了不少新的有意义的现象     

关联噪声驱动多维系统的随机动力学

应用随机方法，将关联高斯白噪声驱动系统的随机动力学由一维随机变量的情况推广到了多维的情况，推导了该情况下的一般福克－普朗克方程．应用以上得到的一般福克－普朗克方程，推得了双模激光系统的福克－普朗克方程．发现关联噪声情况下，双模激光系统的福克－普朗克方程变得更为复杂     

Nonequilibrium dynamic transition in a kinetic Ising model driven by both deterministic modulation and correlated stochastic noises

We report the nonequilibrium dynamical phase transition （NDPT） appearing in a kinetic Ising spin system （ISS） subject to the joint application of a deterministic external field and the stochastic mutually correlated noises simultaneously. A time-dependent Ginzburg-Landau stochastic differential equation, including an oscillating modulation and the correlated multiplicative and additive white noises, was addressed and the numerical solution to the relevant Fokker-Planck equation was presented on the basis of an average-period approach of driven field. The correlated white noises and the deterministic modulation induce a kind of dynamic symmetry-breaking order, analogous to the stochastic resonance in trend, in the kinetic ISS, and the reentrant transition has been observed between the dynamic disorder and order phases when the intensities of multiplicative and additive noises were changing. The dependencies of a dynamic order parameter Q upon the intensities of additive noise A and multiplicative noise M, the correlation λ between two noises, and the amplitude of applied external field h were investigated quantitatively and visualized vividly. Here a brief discussion is given to outline the underlying mechanism of the NDPT in a kinetic ISS driven by an external force and correlated noises.     

两传感器信息融合稳态Kalman滤波器和平滑器

应用Kalman滤波方法,基于Riccati方程,对输入噪声和观测噪声相关,且传感器观测噪声相关的两传感器系统,在线性最小方差最优信息融合准则下,提出了按矩阵加权最优信息融合Kalman滤波器和平滑器。为了计算最优加权阵,提出了局部估计误差互协方差阵的计算公式。同单传感器情况相比,可提高融合估计精度。一个目标跟踪系统的仿真例子说明了其有效性。     

带有色观测噪声的广义系统Kalman滤波器

对于带多传感器的广义线性离散随机系统,应用奇异值分解,并通过对观测方程的状态变换,将带有色观测噪声的系统变换为等价的带相关噪声的两个降阶多传感器子系统.应用Kalman滤波方法,在线性最小方差按块对角阵加权融合准则下,提出了按矩阵加权融合降阶广义Kalman滤波器,可减少计算负担和改善局部滤波精度.为了计算最优加权,给出了局部滤波误差协方差阵的计算公式,证明了融合器和局部滤波器之间的精度关系.一个Monte Carlo仿真例子说明了其有效性.     

关联色噪声对肿瘤细胞生长的影响

我们主要研究了由双色噪声驱动的肿瘤细胞Logistic生长模型.推导了近似福克-普朗克方程,分析了相关参数对定态概率分布函数和平均首通时间的影响,发现色噪声强度和关联时间在对稳态概率密度的影响方面起着相反的作用;平均首通时间随关联噪声时间的增大而增大,随乘性色噪声强度和色交叉关联噪声强度的增大而减小,因而可以通过控制乘...     

关联噪声驱动下单模激光线性模型的随机共振

根据线性化的单模激光输出光强方程，直接计算光强的相关函数，得到光强的功率谱及信噪比的方法，计算了具有δ函数形式关联的两白噪声驱动的单模激光线性模型在输入信号后其输出信噪比，发现了随机共振现象，根据计算结果讨论了噪声强度和噪声间互关联程度对信噪比的影响，由于推导用的是精确解析表达式，因此所有参数不受限制，可以探讨近似方法适用范围外的规律。     

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.