提高GPS定位精度的群调度滤波算法*

为解决现行安全生产科技领域中全球定位系统（GPS）/惯性导航系统（INS）融合系统容易产生较大时变误差的关键问题,满足GPS定位信号的精度和信号数据处理时间的要求,提出1种群调度滤波算法,通过实验仿真,分别与模糊算法、机器学习算法及卡尔曼滤波网络算法进行比较研究。结果表明:群调度滤波算法兼顾了定位精度与数据处理时间,具有较大优势,可在航空安全、船舶安全、应急监测、灾后抢险等安全科技领域广泛应用。     

二维平流扩散模型下的集合卡尔曼滤波模拟同化研究

为提高环境数值预报水平,构建了一个针对污染物扩散的模拟数据同化系统。采用集合卡尔曼滤波方法对二维平流扩散模型的状态变量进行了实时校正,实现污染物浓度的实时模拟预报,完成了敏感性实验中集合数目变化、观测方差变化和同化窗口长度变化研究。比较考察观测点位置与污染源距离不同时的预报效果,探讨了优化条件下的同化策略,提出一种根据距离远近动态调节卡尔曼增益权重的方法。在集合数目较小时,可降低计算代价,得到优化的同化效果。     

Estimating marine biogeochemical rates of the carbonate pH system—A Kalman filter tested

Oxygen (O₂), nitrate (NO₃), dissolved inorganic carbon (DIC) or pCO₂, and pH or total alkalinity (TA), are useful indices of marine chemical, physical and biological processes operating on varying time-scales. Although these properties are increasingly being monitored at high frequency, they have not been extensively used for studying ecosystem dynamics. We test whether we can estimate time-evolving biogeochemical rates (e.g. primary production, respiration, calcification and carbonate dissolution, and nitrification) from synthetic high frequency time-series of O₂, NO₃, DIC, pCO₂, TA or pH. More specifically, a Kalman filter has been implemented in a very simplified biogeochemical model describing the dynamics of O₂, NO₃, DIC and TA and linking the concentration data to biogeochemical fluxes. Different sets of concentration data are assimilated and biogeochemical rates are estimated. The frequency of assimilation required to get acceptable results is investigated and is compared with the frequency of sampling in the field or in controlled experimental settings.Smoothing of the data to remove data noise before assimilation improves the estimation of the biogeochemical rates. The best estimated rates are obtained when assimilating O₂, NO₃ and TA although the assimilation of DIC instead of TA also gives satisfactory results. In case pH or pCO₂ is assimilated rather than DIC or TA, the linearization of the (now nonlinear) observation equation introduces perturbations and the Kalman filter behaves suboptimal. We conclude that, given the resolution of data required, the tool has potential to estimate biogeochemical rates of the carbonate system under controlled settings.     

KALMAN FILTER CALCULATION OF SAMPLING FREQUENCY WHEN DETERMINING ANNUAL MEAN SOLUTE CONCENTRATIONS

ABSTRACT: A linear filter (Kalman filter) technique was used with a Streamflow-concentration model the minimize surface water quality sampling frequencies when determining annual mean solute concentrations with a predetermined allowable error. The Kalman filter technique used the stream discharge interval as a replacement for the more commonly used time interval. Using filter computations, the measurement error variance was minimized within the sample size constraints. The Kalman filter application proposed here is applicable only under several conditions including: monitoring is solely to estimate annual mean concentration; discharge measurement errors are negligible; the Streamflow-concentration model is valid; and monthly samples reflect the total variance of the solute in question.     

SHORT-TERM FORECASTING OF SNOWMELT RUNOFF USING ARMAX MODELS1

ABSTRACT: Time series models of the ARMAX class were investigated for use in forecasting daily riverflow resulting from combined snowmelt/rainfall. The Snowmelt Runoff Model (Martinec-Rango Model) is shown to have a form similar to the ARMAX model. The advantage of the ARMAX approach is that analytical model identification and parameter estimation techniques are available. In addition, previous forecast errors can be included to improve forecasts and confidence limits can be estimated for the forecasts. Diagnostic checks are available to determine if the model is performing properly. Finally, Kalman filtering can be used to allow the model parameters to vary continuously to reflect changing basin runoff conditions. The above advantages result in improved flow forecasts with fewer model parameters.     

Kalman最优平滑器在滑坡位移监测数据处理中的应用

针对传统方法中利用Kalman滤波器来处理物体的形变数据存在精度低的缺点,依据Kalman平滑比滤波的精度要高的原理,提出了将滑坡形变的估计问题视为Kalman平滑问题,而不仅仅是Kalman滤波问题;利用Kalman最优平滑器来处理滑坡位移监测数据,可使滑坡预测预报的精度得到较大提高,并在此基础上给出了一种固定区间平滑算法;最后,以龙羊峡龙西滑坡为例进行了实例分析,结果表明本文提出的方法是可行和有效的。此外,结论中针对实际运用中存在的问题提出了相应的建议。     

关联交叉口交通流模糊变权重组合预测方法

针对单一交通流预测方法存在的局限性和传统交通流组合预测模型中权重不能动态变化的问题,提出一种关联交叉口交通流模糊变权重组合预测方法。先对交叉口交通流的关联性进行分析,并给出关联交叉口的定义;再建立关联交叉口交通流模糊自适应变权重组合预测模型,该模型分别利用Kalman滤波器模型与SVM模型来预测关联交叉口交通流量,然后根据这2个模型预测的误差和交通量的变化趋势,采用模糊逻辑推理方法,对这2个预测模型分别赋予适当的权重。试验结果表明,组合预测模型的最大绝对误差、平均绝对误差和相关系数均明显好于单一的预测方法,分别为9.8%、4.63%和0.99。     

On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models

Nonlinear state-space models have been increasingly applied to study population dynamics and data assimilation in environmental sciences. State-space models can account for process error and measurement error simultaneously to correct for the bias in the estimates of system state and model parameters. However, few studies have compared the performance of different nonlinear state-space models for reconstructing the state of population dynamics from noisy time series. This study compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF) and Bayesian nonlinear state-space models (BNSSM) through simulations. Synthetic population time series were generated using the theta logistic model with known parameters, and normally distributed process and measurement errors were introduced using the Monte Carlo simulations. At higher levels of nonlinearity, the UKF and BNSSM had lower root mean square error (RMSE) than the EKF. The BNSSM performed reliably across all levels of nonlinearity, whereas increased levels of nonlinearity resulted in higher RMSE of the EKF. The Metropolis–Hastings algorithm within the Gibbs algorithm was used to fit the theta logistic model to synthetic time series to estimate model parameters. The estimated posterior distribution of the parameter θ indicated that the 95% credible intervals included the true values of θ (=0.5 and 1.5), but did not include 1.0 and 0.0. Future studies need to incorporate the adaptive Metropolis algorithm to estimate unknown model parameters for broad applications of Bayesian nonlinear state-space models in ecological studies.     

利用集合均方根卡尔曼滤波反演重庆地区SO2源排放

传统的"自下而上"清单方法估算的排放清单,其数据的准确性和时效性存在较大局限.基于集合均方根卡尔曼滤波的源清单反演方法,结合WRF-CMAQ（天气研究和预报模式-公共多尺度空气质量模型）被用于对以清华大学编制的2010年MEIC（中国多尺度排放清单模型）排放清单为基础制作的重庆地区SO₂排放源进行反演试验以解决准确性和时效性问题,试验时间段为2014年10月15-31日,重庆主城17个环境空气质量国控监测点ρ（SO₂）小时观测资料用于反演及检验.结果表明:该方法能够反演重庆地区SO₂源排放量,随着反演次数增加,基于反演排放源预报的ρ（SO₂）预报误差持续减小,反演4次后预报误差达到比较低的稳定的水平,其均方根误差均低于20 μg/m3. 5次反演后SO₂源排放量用于2014年10月24-29日每天起始预报,其预报的站点、时间平均的均方根误差从100~400 μg/m3降至30 μg/m3以下.反演中应用局地化尺度减少集合取样误差影响,54与81 km两个局地化尺度反演结果对预报改善效果相当,表明主要影响重庆主城ρ（SO₂）的源排放位于主城及周边地区,也说明内源排放对重庆主城ρ（SO₂）起主要影响.反演后面源排放量主城区降幅约为30 kg/（d·km2）,周边地区减少10~20 kg/（d·km2）,主城区部分SO₂点源排放量降幅约为25 kg/（d·km2）,说明2010年MEIC排放清单高估了试验时段重庆地区的SO₂排放.      

1种抑制巷道信号NLOS的矿井RSSI高精度定位算法*

在RSSI（Received Signal Strength Indication）测距定位技术中,为抑制巷道信号NLOS（Non Line of Sight）传输对定位结果的影响,提出信号指纹定位和几何优化算法。在离线阶段利用高斯滤波最大值加权法和最小二乘法建立符合矿井巷道环境的无线信号测距模型,设计改进卡尔曼滤波器平滑处理离线信号值,抑制巷道信号NLOS传输带来的影响,建立离线信号指纹库;在线定位阶段,利用加权K最近邻法（WKNN）将定位目标接收到的信号值与指纹库中的信号值进行匹配,将匹配到的最优信号值参与测距定位计算,最后通过几何优化算法将定位结果归一化处理,使其符合一维定位分布。结果表明:所提算法的平均定位误差为0.9 m,相比于高斯滤波最大值加权法、经典卡尔曼滤波指纹定位算法和改进卡尔曼滤波指纹定位方法,其平均误差分别减小2.36,1.17,0.35 m。所提算法能够有效抑制巷道信号NLOS传输对RSSI测距定位的影响,可实现RSSI方法在矿井NLOS环境中的有效应用。