基于ICCP算法的重力辅助惯性导航

迭代最近等值线算法(ICCP)是一种重要的匹配导航算法,文中首先介绍ICCP算法的基本原理,随后在0.2′×0.2′重力异常数据库的基础上,利用ICCP算法进行仿真计算得到最佳匹配位置。最后为了验证匹配位置是否可用于修正惯导误差,提出将匹配位置误差作为观测量,用卡尔曼滤波对惯导系统误差进行最优估计。由最后的仿真结果可以看出,ICCP算法可有效抑制惯导纬度误差的增长,且最大纬度误差不超过2,′以匹配位置误差作为观测量可以用来估计惯导方位误差角。     

A way to synchronize models with seismic faults for earthquake forecasting: Insights from a simple stochastic model

Numerical models are starting to be used for determining the future behaviour of seismic faults and fault networks. Their final goal would be to forecast future large earthquakes. In order to use them for this task, it is necessary to synchronize each model with the current status of the actual fault or fault network it simulates (just as, for example, meteorologists synchronize their models with the atmosphere by incorporating current atmospheric data in them). However, lithospheric dynamics is largely unobservable: important parameters cannot (or can rarely) be measured in Nature. Earthquakes, though, provide indirect but measurable clues of the stress and strain status in the lithosphere, which should be helpful for the synchronization of the models.The rupture area is one of the measurable parameters of earthquakes. Here we explore how it can be used to at least synchronize fault models between themselves and forecast synthetic earthquakes. Our purpose here is to forecast synthetic earthquakes in a simple but stochastic (random) fault model. By imposing the rupture area of the synthetic earthquakes of this model on other models, the latter become partially synchronized with the first one. We use these partially synchronized models to successfully forecast most of the largest earthquakes generated by the first model. This forecasting strategy outperforms others that only take into account the earthquake series. Our results suggest that probably a good way to synchronize more detailed models with real faults is to force them to reproduce the sequence of previous earthquake ruptures on the faults. This hypothesis could be tested in the future with more detailed models and actual seismic data.     

基于卡尔曼滤波的时间序列影像背景估计

文章将卡尔曼滤波理论应用于时间序列影像的背景估计,引入衰减记忆因子解决滤波的发散问题,最终实现了对背景信号的估计。     

一种新的综合Kalman滤波及其在星载GPS 低轨卫星定轨中的应用

针对将Kalman滤波方法应用到星载GPS定轨时，由于动态噪声和观测噪声确定不准而造成滤波的发散、污染观测值造成Kalman滤波估值的扭曲及计算舍入误差可能带来协方差阵的不正定性等缺陷，提出了一种新的综合Kalman滤波方法。该方法用拟准检定法准确地探测和修正量测方程中存在的粗差；用UD分解算法克服了数值的不稳定性，改进了计算精度；用Sage自适应滤波器克服滤波器的发散。算例结果表明，这种综合卡尔曼滤波方法具有数值稳定性好、较强的自适应性和较好地削弱粗差影响等优点。     

伴随模式同化系统在修正模式地形中的应用

任何模式都只是实际大气的一种近似模拟，存在误差。鉴于传统的四维资料伴随模式同化系统都是假设模式完全正确仅对初始场进行修正。将伴随模式同化系统应用于修正模式地形误差，通过对不同初始地形的修正试验表明，MM5伴随模式同化系统能很好地对地形误差进行修正，能够反演出与初始气象要素场分辨率相匹配、与模式更协调的地形场，为伴随模式同化系统的广泛应用提供了一种新的思路。     

变分同化方法在Lorenz系统中的简单应用研究

利用Lorenz模式作变分同化数值试验，通过对一个简单系统的讨论，介绍四维变分同化方法。对初值敏感性和观测点的个数及观测值作了对比试验，发现随着模式对初值敏感性的增加，同化效果会越来越差；观测点越少，观测值误差越大，这些都会影响同化效果，甚至导致同化失败。     

改进的切线性模式对一个边界层模式变分资料同化的改善

大气、海洋中边界层物理过程的强非线性会对切线性模式近似（尤其是当积分时间长时）有较大的影响，从而给相应的4维变分资料同化问题的求解造成困难。本构造了Mellor-Yamada湍流封闭模式（level 2.5)的一个改进的切线性模式，相比通常的切线性模式和简化的切线性模式可以提高对非线性扰动的逼近。利用这个改进的切线性模式的伴随模式分别进行了1到7天的变分资料同化试验，得到了满意的结果。而用通常的伴随模式和简化的伴随模式都无法得到令人满意的结果。     

Parameter identification of inelastic structures under dynamic loads

The generalized model of differential hysteresis contains 13 control parameters with which it can curve‐fit practically any hysteretic trace. Three identification algorithms are developed to estimate the control parameters of hysteresis for different classes of inelastic structures. These algorithms are based upon the simplex, extended Kalman filter, and generalized reduced gradient methods. Novel techniques such as global search and internal constraints are incorporated to facilitate convergence and stability. Effectiveness of the devised algorithms is demonstrated through simulations of two inelastic systems with both pinching and degradation characteristics in their hysteretic traces. Owing to very modest computing requirements, these identification algorithms may become acceptable as a design tool for mapping the hysteretic traces of inelastic structures. Copyright © 2002 John Wiley & Sons, Ltd.     

On the oceanic impact of a data-assimilation method in a coupled ocean-land-atmosphere model

 Based on the Kalman filter theory, a new data-assimilation method has been used to improve the 3-D oceanic temperature field of the Center for Ocean-Land-Atmosphere Studies (COLA) coupled general circulation model. This method is applied to assimilate surface and subsurface temperature of in situ measurements from the Pilot Research Moored Array in the Tropical Atlantic project (PIRATA). The assimilation of the PIRATA data produces an improved representation of the thermal state of the ocean and allows a better estimation of other oceanographic quantities, like meridional heat fluxes and zonal currents. The present paper focuses on the tropical Atlantic and, in particular, it contains new reconstructed temperature profiles. One-month forecast experiments during 1999 were performed and the impact of the assimilation is discussed. Received: 24 April 2001 / Accepted: 8 March 2002