GPS车载导航的交互式多模型算法

GPS动态定位数据的处理广泛采用卡尔曼滤波技术,而应用卡尔曼滤波要求运动模型准确可靠,但由于载体真实运动的复杂多变,任何单一模型都难以全面描述,致使单一模型的滤波都容易出现模型误差。针对这一问题,将机动目标跟踪领域广泛应用的交互式多模型算法引入到车载导航中。通过分析车辆的运动特点,选取匀速直线模型和当前统计模型进行交互;同时考虑到车载终端计算能力有限,将状态变量在各方向解耦。仿真显示,在机动时改进的算法和单一模型的自适应算法基本相当,但在非机动时改进的算法明显占优。     

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利用交互多模型的动态精密单点定位故障诊断算法

提出了一种基于相邻历元模型概率比的交互多模型方法,以提高故障诊断的效率,控制异常数据对定位结果的影响。首先,根据故障可能发生的情况建立交互多模型,以便对故障进行定位;然后,根据相邻历元的故障模型概率比对故障进行诊断,提高故障诊断的正确率;最后,根据抗差估计原理对故障进行处理,控制异常影响,提高动态精密单点定位的精度。以实测动态GPS数据为例,此模型可以提高故障诊断的正确率,控制异常影响,进而提高动态精密单点定位的精度。     

快速交互式多模型算法的导航定位解算

针对卫星导航定位中单模型在急速转弯条件下出现较大偏差和交互式多模型系统运算量大的问题,该文提出了一种快速的交互式多模型算法。该算法先对CS模型进行改进,然后利用CV模型和改进的CS模型构成多模型系统,结合修正的滤波发散判据与次优的极大后验估计,根据载体实际的运动状态自行调整扩展卡尔曼滤波采用的运动模型,从而弥补了单模型描述复杂运动的不足。实验结果表明:与单模型相比,该方法有效解决了急速转弯误差大的问题;与标准交互式多模型相比,该方法对位置的估计精度提高了39.4%,而运算时间却缩短了47.4%。     

Inclusive Multiple Models(IMM) for predicting groundwater levels and treating heterogeneity

An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.