Membrane protein native state discrimination by implicit membrane models

Four implicit membrane models [IMM1, generalized Born (GB)‐surface area‐implicit membrane (GBSAIM), GB with a simple switching (GBSW), and heterogeneous dielectric GB (HDGB)] were tested for their ability to discriminate the native conformation of five membrane proteins from 450 decoys generated by the Rosetta‐Membrane program. The energy ranking of the native state and Z‐scores were used to assess the performance of the models. The effect of membrane thickness was examined and was found to be substantial. Quite satisfactory discrimination was achieved with the all‐atom IMM1 and GBSW models at 25.4 Å thickness and with the HDGB model at 28.5 Å thickness. The energy components by themselves were not discriminative. Both van der Waals and electrostatic interactions contributed to native state discrimination, to a different extent in each model. Computational efficiency of the models decreased in the order: extended‐atom IMM1 > all‐atom IMM1 > GBSAIM > GBSW > HDGB. These results encourage the further development and use of implicit membrane models for membrane protein structure prediction. © 2012 Wiley Periodicals, Inc.     

一种基于IMM的自适应跟踪数据率算法

针对交互式多模型(IMM),提出一种自适应跟踪数据率的算法.该算法利用IMM模型更新概率对各模型产生的采样间隔时间进行混合估计,产生系统的自适应采样间隔时间.在保证跟踪精度的基础上,有效地降低了跟踪采样次数.仿真结果表明了方法的有效性.     

一种新的基于交互多模型的序贯重要采样算法

通过将交互多模型（IMM）算法和粒子滤波（SIS）算法结合,提出了一种新的IMM～SIS算法。在每个模型中,都有一个标准的粒子滤波器,模型之间的交互与传统的IMM一样。由于在新的算法中,每个模型中粒子滤波都保证固定数量的粒子,因此不会出现粒子退化和贫乏现象。仿真证明了新的IMM—SIS算法在收敛速度和精度方面都要优于传统的IMM—EKF算法。     

改进的交互式多模型粒子滤波跟踪算法

通常的交互多模型卡尔曼滤波(IMMKF)或交互多模型扩展卡尔曼滤波(IMMEKF)对于非高斯问题无能为力;对于非线性问题,其性能不及交互多模型粒子滤波算法(IMMPF)。粒子滤波能够处理非线性/非高斯问题,其与交互式多模型结合用来获得更好的跟踪性能。然而,粒子滤波的主要问题是巨大的计算量,由于粒子滤波通常采用大量的粒子数目,将带来很大的计算负荷。该文提出了一种改进的交互多模型粒子滤波算法,其利用多模型综合使用了卡尔曼滤波和粒子滤波,与常规交互式多模型粒子滤波(IMMPF)相比,大大改善了计算效率。对于非线性/非高斯问题,其性能与IMMPF相当;对于线性问题,其性能与IMMEKF相当,并优于IMMPF的性能。     

A new IMM algorithm using fixed coefficients filters (fastIMM)

In this paper we present an new approach based on two filters αβ$\alpha \beta$ and αβγ$\alpha \beta \gamma$ using Interacting Multiples Models (IMM) design instead of a Kalman filter second and third order for the tracking a single maneuver target. The comparison between the proposed filter and the IMM improves the computing time amount about 60% while having a high accuracy.     

改进的交互式当前统计模型算法

交互式多模型(IMM)算法是一种有效的机动目标跟踪算法,但其性能与模型的选择、个数以及参数有关,而当前统计模型(CSM)是一种模型自适应的方法.因此,利用CSM为IMM提供模型,提出一种改进的交互式当前统计模型算法.仿真结果表明,该算法能有效地提高机动目标跟踪精度.     

一种可行的高超声速飞行器跟踪算法

通过分析临近空间高超声速飞行器的运动特性,建立了高超声速飞行器运动模型,并运用交互式多模型(IMM)算法对其进行跟踪,通过100次蒙特卡罗实验,获得了误差较小的理想跟踪效果,说明了IMM算法用于高超声速目标跟踪中的可行性.     

红外单站多目标跟踪算法研究

利用IRST(红外搜索与跟踪)系统所获取的各目标的角度及其红外光谱辐射功率和信息，通过对红外光谱辅射功率和的相关处理，运用选优的JPDA（联合概率数据关联）算法与IMM（交互多模型）算法实现了IRST系统的单站多目标跟踪，并通过两个仿真场景对算法性能进行了检验.仿真结果表明：在跟踪开始阶段，两个场景中的每个目标都能获得高精度的跟踪;当目标编队飞行时，算法能对各目标进行有效的跟踪，而且跟踪精确度也是令人满意的;当目标交叉飞行时，跟踪的误差明显加大，随着时间的延续，对远距离目标会失去跟踪能力，但对近距离目标仍能进行有效的跟踪.     

基于多模型交互的红外目标跟踪方法研究

针对非线性运动的红外弱小目标跟踪问题,设计了一种基于交互式多模型性的跟踪方法。首先,引入多个运动模型,然后利用无迹卡尔曼滤波计算每个模型的滤波器状态估计,最后按照一定的概率加权得到总的状态估计值。使用数学仿真和物理实验对该方法进行了去噪和预测的功能验证,实验结果及相应理论分析表明该系统可有效的实现复杂背景下的红外弱小目标跟踪。     

Unified Maneuvering Model for Target Tracking by Using Range-rate Measurement

A novel Unified maneuvering model （UMM） for tracking the target performed the complex curvilinear motion in two dimensions is presented, where the model is constructed by time-varying parameters and driven by the along-track and cross-track acceleration in- put, and the range rate plays a key role to update vari- able parameters online. In Cartesian coordinates, by incorporating the cross-track acceleration component which is estimated online by using the range-rate, and the along- track acceleration component which is determined under the assumption of zero-mean first order Markovian process, the proposed UMM exhibits highly self-adjustment capability to compensate the mismatch between the actual motion and the mathematic model adaptively, espe-cially, shows a well capability to approximate the standard Interacting multiple model （IMM） under the situation of complex curvilinear motion and a higher level of the measurement noise. Simulation results validate our theory and show that UMM-based filtering is superior to that using the Current statistic model （CSM）, and has a well approx- imation to IMM, meanwhile, has a low computational load substantially.