Impact of Multiresolution Active and Passive Microwave Measurements on Soil Moisture Estimation Using the Ensemble Kalman Smoother

An observing system simulation experiment is developed to test tradeoffs in resolution and accuracy for soil moisture estimation using active and passive L-band remote sensing. Concepts for combined radar and radiometer missions include designs that will provide multiresolution measurements. In this paper, the scientific impacts of instrument performance are analyzed to determine the measurement requirements for the mission concept. The ensemble Kalman smoother (EnKS) is used to merge these multiresolution observations with modeled soil moisture from a land surface model to estimate surface and subsurface soil moisture at 6-km resolution. The model used for assimilation is different from that used to generate "truth." Consequently, this experiment simulates how data assimilation performs in real applications when the model is not a perfect representation of reality. The EnKS is an extension of the ensemble Kalman filter (EnKF) in which observations are used to update states at previous times. Previous work demonstrated that it provides a computationally inexpensive means to improve the results from the EnKF, and that the limited memory in soil moisture can be exploited by employing it as a fixed lag smoother. Here, it is shown that the EnKS can be used in large problems with spatially distributed state vectors and spatially distributed multiresolution observations. The EnKS-based data assimilation framework is used to study the synergy between passive and active observations that have different resolutions and measurement error distributions. The extent to which the design parameters of the EnKS vary depending on the combination of observations assimilated is investigated     

Variational data assimilation of microwave radiobrightnessobservations for land surface hydrology applications

Our ability to accurately describe large-scale variations in soil moisture is severely restricted by process uncertainty and the limited availability of appropriate soil moisture data. Remotely sensed microwave radiobrightness observations can cover large scales but have limited resolution and are only indirectly related to the hydrologic variables of interest. The authors describe a four-dimensional (4D) variational assimilation algorithm that makes best use of available information while accounting for both measurement and model uncertainty. The representer method used is more efficient than a Kalman filter because it avoids explicit propagation of state error covariances. In a synthetic example, which is based on a field experiment, the authors demonstrate estimation performance by examining data residuals. Such tests provide a convenient way to check the statistical assumptions of the approach and to assess its operational feasibility. Internally computed covariances show that the estimation error decreases with increasing soil moisture. An adjoint analysis reveals that trends in model errors in the soil moisture equation can be estimated from daily L-band brightness measurements, whereas model errors in the soil and canopy temperature equations cannot be adequately retrieved from daily data alone. Nonetheless, state estimates obtained from the assimilation algorithm improve significantly on prior model predictions derived without assimilation of radiobrightness data     

一种基于单轴正反旋转的惯性器件误差估计

为了提高捷联惯导导航精度,构建一种Kalman滤波模型来估计陀螺常值漂移和加速度计零偏。首先分析了载体作单轴正、反旋转运动时,捷联惯导的系统误差特性,然后以正、反旋转两过程中的姿态误差和速度误差为状态变量,以两过程中同一位置处的姿态误差差值和速度误差和值为观测变量,构建了一种Kalman滤波模型,来估计惯性器件常值误差;经可观测性分析,该模型是可观测的。仿真实验中,对于3个陀螺漂移均为0.1(°)/h、加速度计零偏均为9.78×10~(-3 )m/s~2的捷联惯导,陀螺漂移估计精度达到0.01(°)/h,水平方向加速计零偏估计误差均小于0.4×10~(-3 )m/s~2,实验证明该方案可行。     

多源遥感数据与水文过程模型的土壤水分同化方法研究

提出一种基于集合卡尔曼滤波的一维数据同化系统,对不同深度土壤层的水分含量进行同化,该系统的模型算子为分布式水文模型,观测算子是积分方程模型和条件温度指数模型.于2008年6月1日至7月2日在黑河流域进行了同化实验,结果表明,集合卡尔曼滤波能较好地处理强非线性问题,与单独DHSVM模型模拟土壤水分含量相比,同化的表层和根层土壤水分含量精度有明显提高,其中盈科站表层的均方根误差和平均误差分别减小了0.021 7和0.032 9,根层的均方根误差和平均误差分别减小了0.019 3和0.025;临泽站的精度也有明显提高,表明多源遥感数据的同化在地表土壤水分含量的估计中具有较大的潜力.     

基于卡尔曼滤波的动态轨迹预测算法

基于拟合的传统轨迹预测算法已无法满足高精度和实时性预测要求.提出基于卡尔曼滤波的动态轨迹预测算法,对移动对象动态行为进行状态估计,利用前一时刻的估计值和当前时刻的观测值更新对状态变量的估计,进而对下一时刻的轨迹位置预测.大量真实移动对象数据集上的实验结果表明：GeoLife数据集上基于卡尔曼滤波的轨迹预测算法的平均预测误差（预测轨迹点与实际轨迹点的均方根误差）为12.5米;与基于轨迹拟合的轨迹预测算法相比,T-Drive数据集预测误差平均下降了555.4米,预测准确率提升了7.1%.在保证预测时效性前提下,基于卡尔曼滤波的动态轨迹预测算法解决了轨迹预测精度较低的问题.     

三状态样条Kalman滤波与目标机动检测

基于样条方法和Kalman滤波理论，给出了一种新的三状态样条Kalman滤波方法及对目标进行机动检测的方法。该滤波方法用样条函数建立目标运动模型，应用Kalman滤波进行状态估计；该机动检测方法通过检测滤波新息来对目标机动发生或消除进行判断。由于考虑了加速度的实时估计，该滤波方法尤其适用于对弹道式目标的机动跟踪。仿真计算与实测数据计算表明：该滤波具有较高的精度；该机动检测方法具有较高的检测效率。     

UPF算法在惯导非线性初始对准中的应用

惯性导航系统（INS）的初始对准误差模型通常为非线性的,对于估计惯导误差普遍采用的是扩展卡尔曼滤波算法（EKF）,该方法是在一阶泰勒展开的基础上近似得到的,因而误差较大。粒子滤波算法一种新颖的非线性滤波算法,它较传统的EKF算法具有稳定性好,适用范围广的优点。该文首先介绍了作为粒子滤波理论基础的递推贝叶斯估计的基本概念,说明了重要性函数对于粒子滤波器的设计是至关重要的。随后,给出了一种将不敏卡尔曼滤波（UKF）算法作为重要性函数的UPF算法,并提出将其用于静基座条件下的惯导系统非线性初始对准,通过计算机仿真对比了UPF和EKF的估计效果。仿真结果表明,UPF算法较传统的EKF算法对准时间更快,对准精度更高。     

A new motor speed estimator using Kalman filter in low-speed range

In this paper, a new machine drive technique using novel estimation strategy for the very low-speed operation to estimate both the instantaneous speed and disturbance load torque is proposed. In the proposed algorithm, a Kalman filter is incorporated to estimate both the motor speed and the disturbance torque. The Kalman filter is an optimal state estimator and is usually applied to a dynamic system that involves a random noise environment. The effects of parameter variations are discussed, and it is verified that the system is stable to the modeling error. Experimental results confirm the validity of the proposed estimation technique     

A land surface process/radiobrightness model with coupled heat andmoisture transport in soil

Heat and moisture transport in soil are coupled processes that jointly determine temperature and moisture profiles. The authors present a physically based, one-dimensional (1D), coupled heat and moisture transport hydrology (1-DH) model for bare, unfrozen, moist soils subject to insolation, radiant heating and cooling, and sensible and latent heat exchanges with the atmosphere. A 60-day simulation is conducted to study the effect of dry-down on soil temperature and moisture distributions in summer for bare soil in the Midwest United States. Given a typical initial moisture content of 38% by volume, the authors find that temperature differences between the water transport and no water transport cases exhibit a diurnal oscillation with a slowly increasing amplitude, but never exceed 4.4 K for the 60-day period. However, moisture content of the surface decreases significantly with time for the water transport case and becomes only about 21% at the end of the same period. The 1-DH model is linked to a radiobrightness (1-DH/R) model as a potential means for soil moisture inversion. The model shows that radiobrightness thermal inertia (RTI) correlates with soil moisture if the two radiobrightnesses are taken from times near the thermal extremes, e.g., 2 a.m. and 2 p.m., and that RTI appears temperature-dependent at the ending stages of the drydown simulations where soils are dry and their moisture contents vary slowly. Near times of thermal crossover, the RTI technique is insensitive to soil moisture     

基于导频和修正Kalman滤波的MIMO-OFDM信道估计方法

提出了一种适用于时间频率选择性衰落信道的MIMO-OFDM系统的组合信道估计方法。采用AR过程对信道进行建模,利用基于导频的低维Kalman滤波算法进行信道估计,并采用LS算法估计时变的信道衰减因子。Kalman滤波跟踪了信道的时域相关性,为了同时跟踪信道的频域相关性,采用了一种基于MMSE(minimum mean square error)的合并器对Kalman滤波算法进行修正。仿真表明,提出的这种组合算法降低了传统的Kalman滤波结构的复杂度,能够跟踪信道的时频变化,改进了基于LS准则的信道估计算法,并且与复杂的高维Kalman滤波算法的信道估计性能相当。     

基于最大熵Kalman滤波的机载SINS自标定技术

针对在机载捷联惯导系统(SINS)自标定过程中,量测噪声呈非高斯分布,导致经典Kalman滤波性能降低的问题,该文提出了基于最大熵Kalman滤波(MCKF)的机载SINS自标定技术。该方法采用最大相关熵准则(MCC)替代经典Kalman滤波的最小均方误差准则,有效利用信号的高阶矩信息,并将其应用于机载SINS自标定系统中。仿真结果表明,在非高斯噪声条件下,该方法能够估计出机载SINS待标定参数,且算法的鲁棒性和误差项估计精度均优于经典Kalman滤波,具有一定的工程应用价值。     

An Integrated Approach for the Estimation of Mobile Subscriber Geolocation

The continuous growth of wireless services market, fuels the need for precise location dependent services, leading researchers from academia and industry to reassess existing geolocation methods regarding accuracy and availability of position estimation. The proposed method for mobile subscriber geolocation utilizes key concepts from estimation theory and specifically the Kalman filter algorithm to determine an optimal estimate on the actual system state (which primarily includes location, velocity) based on the observations acquired by employing network- or terminal-based techniques, which are briefly presented and assessed thereafter. Given the proven limitations of individual techniques, the alternative strategies for fusion of data are outlined, the details of the operation of a fusion scheme based on the Kalman filter are discussed and the impact of the proposed work over conventional methodologies is quantified.     

The NAFE'05/CoSMOS Data Set: Toward SMOS Soil Moisture Retrieval, Downscaling, and Assimilation

The National Airborne Field Experiment 2005 (NAFE'05) and the Campaign for validating the Operation of Soil Moisture and Ocean Salinity (CoSMOS) were undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. The objective of the joint campaign was to provide simulated Soil Moisture and Ocean Salinity (SMOS) observations using airborne L-band radiometers supported by soil moisture and other relevant ground data for the following: (1) the development of SMOS soil moisture retrieval algorithms; (2) developing approaches for downscaling the low-resolution data from SMOS; and (3) testing its assimilation into land surface models for root zone soil moisture retrieval. This paper describes the NAFE'05 and CoSMOS airborne data sets together with the ground data collected in support of both aircraft campaigns. The airborne L-band acquisitions included 40 km times 40 km coverage flights at 500-m and 1-km resolution for the simulation of a SMOS pixel, multiresolution flights with ground resolution ranging from 1 km to 62.5 m, multiangle observations, and specific flights that targeted the vegetation dew and sun glint effect on L-band soil moisture retrieval. The L-band data were accompanied by airborne thermal infrared and optical measurements. The ground data consisted of continuous soil moisture profile measurements at 18 monitoring sites throughout the 40 km times 40 km study area and extensive spatial near-surface soil moisture measurements concurrent with airborne monitoring. Additionally, data were collected on rock coverage and temperature, surface roughness, skin and soil temperatures, dew amount, and vegetation water content and biomass. These data are available at www.nafe.unimelb.edu.au.     

Modified Unscented Kalman Filter for a Multirate INS/GPS Integrated Navigation System

Instead of the extended Kalman filter, the unscented Kalman filter (UKF) has been used in nonlinear systems without initial accurate state estimates over the last decade because the UKF is robust against large initial estimation errors. However, in a multirate integrated system, such as an inertial navigation system (INS)/Global Positioning System (GPS) integrated navigation system, it is difficult to implement a UKF‐based navigation algorithm in a low‐grade or mid‐grade microcontroller, owing to a large computational burden. To overcome this problem, this letter proposes a modified UKF that has a reduced computational burden based on the basic idea that the change of probability distribution for the state variables between measurement updates is small in a multirate INS/GPS integrated navigation filter. The performance of the modified UKF is verified through numerical simulations.     

基于CKF的系统误差与目标状态联合估计算法

针对量测信息中系统误差对目标状态估计精度造成 的不利影响,提出了一种基于容积卡尔曼滤波(CKF)的系统 误差与状态联合估计(JE-CKF)算法。在算法实现中,首先采用状态向量维数扩展方法建立 非线性滤波框架下的系统误差配 准模型,其次根据系统误差配准模型对量测信息中的系统误差进行估计,进而通过对CKF实 现中量测预测值 的修正,改善量测残差中系统误差对滤波精度的影响。理论分析和仿真结果验证了算法的可 行性和有效性。     

基于角度约束采样的单站无源定位混合粒子滤波算法

为实现固定单站对运动辐射源的快速定位,该文给出了一种基于角度约束采样的混合粒子滤波算法。该算法从EKF(Extended Kalman Filter)滤波得到建议分布,利用角度测量对状态变量的约束关系从建议分布产生所需粒子,可以减少粒子滤波用于高维情况时所需的粒子数目,改善滤波性能,降低运算成本。结合利用多普勒变化率和角度测量的单站定位方法,与EKF,UKF(Unscented Kalman Filter)以及一般混合粒子滤波算法的仿真比较表明,该算法在滤波收敛速度、跟踪精度以及稳定性方面优于其它算法,估计误差更接近Cramer-Rao下界。     

改进的平方根容积卡尔曼滤波及其在POS中的应用

为解决扩展卡尔曼滤波在处理复杂非线性状态估计时,存在收敛速度慢、估计精度低及数值稳定性差等问题,引入一种改进的平方根容积卡尔曼滤波算法(A-SRCKF)。该算法在容积卡尔曼滤波基础上引入矩阵QR分解、Cholesky分解因数更新等技术,避免了矩阵分解、求逆及求导等复杂运算,极大降低了计算复杂度;并针对系统时变及统计特性未知情况下量测噪声协方差阵难以获取问题,通过引入自适应噪声估计器并结合小波卡尔曼滤波思想,构造出加权量测噪声协方差阵,提高了数值精度及稳定性。将A-SRCKF应用于机载定姿定位系统中,仿真结果表明:该算法有效地提升了估计精度,并且运行速度较快。     

IMM-based Kalman filter for channel estimation in UWB OFDM systems

Ultra-wide band (UWB) communication is one of the most promising technologies for high-data rate wireless networks for short-range applications. This paper proposes a blind channel estimation method namely Interactive Multiple Model (IMM)-based Kalman algorithm for UWB OFDM systems. IMM-based Kalman filter is proposed to estimate frequency selective time-varying channel. In the proposed method, two Kalman filters are concurrently estimating channel parameters. The first Kalman filter, namely the Static Model Filter (SMF) gives an accurate result when the user is static while the second Kalman filter namely the Dynamic Model Filter (DMF) gives an accurate result when the receiver is in moving state. The static transition matrix in SMF is assumed as an Identity matrix where as in DMF, it is computed using Yule–Walker equations. The resultant filter estimate is computed as a weighted sum of individual filter estimates. The proposed method is compared with other existing channel estimation methods.     

主被动雷达复合导引头信息融合技术研究

针对主被动雷达复合导引头,研究了基于序贯扩展Kalman滤波的信息融合算法。利用主被动雷达复合导引头对目标角误差进行观测,将匹配后的测量角度进行最优加权,进而以角度信息作为量测,估计目标的运动信息。通过试验验证,基于主被动雷达信息融合状态估计比仅依赖主动雷达观测量的状态估计稳态误差小,且滤波器收敛速度更快。