De-correlated unbiased sequential filtering based on best unbiased linear estimation for target tracking in Doppler radar

In target tracking applications,the Doppler measurement contains information of the target range rate,which has the potential capability to improve the tracking performance.However,the nonlinear degree between the measurement and the target state increases with the introduction of the Doppler measurement.Therefore,target tracking in the Doppler radar is a nonlinear filtering problem.In order to handle this problem,the Kalman filter form of best linear unbiased estimation(BLUE)with position measurements is proposed,which is combined with the sequential filtering algorithm to handle the Doppler measurement further,where the statistic characteristic of the converted measurement error is calculated based on the predicted information in the sequential filter.Moreover,the algorithm is extended to the maneuvering target tracking case,where the interacting multiple model(IMM)algorithm is used as the basic framework and the model probabilities are updated according to the BLUE position filter and the sequential filter,and the final estimation is a weighted sum of the outputs from the sequential filters and the model probabilities.Simulation results show that compared with existing approaches,the proposed algorithm can realize target tracking with preferable tracking precision and the extended method can achieve effective maneuvering target tracking.

Parametric estimation for the simple linear regression model under moving extremes ranked set sampling design

Cost effective sampling design is a major concern in some experiments especially when the measurement of the characteristic of interest is costly or painful or time consuming.Ranked set sampling(RSS)was first proposed by McIntyre[1952.A method for unbiased selective sampling,using ranked sets.Australian Journal of Agricultural Research 3,385-390]as an effective way to estimate the pasture mean.In the current paper,a modification of ranked set sampling called moving extremes ranked set sampling(MERSS)is considered for the best linear unbiased estimators(BLUEs)for the simple linear regression model.The BLUEs for this model under MERSS are derived.The BLUEs under MERSS are shown to be markedly more efficient for normal data when compared with the BLUEs under simple random sampling.

Estimation of a Linear Model in Terms of Intra-Class Correlations of the Residual Error and the Regressors

Objectives: The objective is to analyze the interaction of the correlation structure and values of the regressor variables in the estimation of a linear model when there is a constant, possibly negative, intra-class correlation of residual errors and the group sizes are equal. Specifically: 1) How does the variance of the generalized least squares (GLS) estimator (GLSE) depend on the regressor values? 2) What is the bias in estimated variances when ordinary least squares (OLS) estimator is used? 3) In what cases are OLS and GLS equivalent. 4) How can the best linear unbiased estimator (BLUE) be constructed when the covariance matrix is singular? The purpose is to make general matrix results understandable. Results: The effects of the regressor values can be expressed in terms of the intra-class correlations of the regressors. If the intra-class correlation of residuals is large, then it is beneficial to have small intra-class correlations of the regressors, and vice versa. The algebraic presentation of GLS shows how the GLSE gives different weight to the between-group effects and the within-group effects, in what cases OLSE is equal to GLSE, and how BLUE can be constructed when the residual covariance matrix is singular. Different situations arise when the intra-class correlations of the regressors get their extreme values or intermediate values. The derivations lead to BLUE combining OLS and GLS weighting in an estimator, which can be obtained also using general matrix theory. It is indicated how the analysis can be generalized to non-equal group sizes. The analysis gives insight to models where between-group effects and within-group effects are used as separate regressors.

基于BLUE的雷达/红外异步融合算法研究

多传感器融合是提高态势感知能力的重要手段。为提高探测能力,将雷达和红外传感器组网,各传感器独立工作,在统一调度下,完成探测、跟踪、识别任务。研究该系统的雷达/红外数据融合算法,针对传感器异步探测特点,采用观测驱动的融合跟踪方法:雷达探测到目标时,采用基于状态预测的改进BLUE(Best Lin-ear Unbiased Estimation)滤波,通过方位预测的辅助,减小测角误差非线性效应,提高跟踪性能;红外探测到目标时,基于方位预测和斜距观测,构造新的转换量测模型,实现基于不完备观测的修正BLUE滤波器。理论分析和仿真证明:所述雷达/红外数据融合方法,在不同传感器布局下都具备更优的综合性能,其设计思想可解决其他类似的多传感器融合问题,有较好的应用推广潜力。

基于短期观测记录的重现期风速预测

在偏僻地区或山区建设重要建筑物、构筑物或桥梁时,通常在附近安装风速观测仪器,获得至少1年的短期风速记录。提出了根据短期风速记录预测指定重现期风速极值及风压方法,为结构抗风设计提供了科学依据。提出独立风暴法提取短期风速记录的最大值,建立极值I型概率分布模型,采用最好线性无偏估计方法确定概率分布参数。依据短期风速最大值概率与年风速最大值概率的变换关系,建立年发生最大值的概率分布及重现期风速、风压的表达式。算例结果表明,重现期风速预测值的离散性与风速阈值有关,在预测值离散系数较小的情况下可确定重现期风速及其置信区间。

基于分布式BLUE的多雷达数据融合方法

针对线性系统与非线性观测的混合跟踪融合问题,推导目标匀加速运动时的三维最佳线性无偏估计(BLUE)滤波器,给出基于分布式BLUE滤波的多雷达数据融合方法。由各单元雷达对直角坐标系下的目标状态进行BLUE估计,对多部雷达的目标状态在融合中心进行融合估计,采用位置、速度均方根误差和平均归一化估计误差平方作为融合性能评价标准。仿真结果表明,与基于NC的去偏转换状态融合方法和基于MMC的无偏转换状态融合方法相比,该方法对于过程噪声和测量噪声的变化不敏感,比基于NC和MMC的方法具有更小的位置和速度均方根误差,即使在大误差的情况下,仍然具有较高的融合精度和可靠性。

极坐标系下可处理多普勒量测的BLUE跟踪算法

事实已表明包含目标速度信息的多普勒量测具有有效提高目标状态估计精度的潜力.该文在直角坐标系下提出两种可使用转换多普勒量测(即距离量测与多普勒量测的乘积)的滤波器,一种借助了构造的多普勒伪状态,另一种没有借助多普勒伪状态.从理论上讲,它们都是在最佳线性无偏估计准则下的最优线性无偏滤波器,并且避免了量测转换方法的根本缺陷.通过将近似处理后的两种新型最优线性滤波器与目前几种流行的方法进行仿真比较,验证了所提出的滤波器的优越性.

基于高程补偿的BLUE算法在塔康中的应用研究

基于塔康系统的斜距、方位和高程可对目标定位,但较大的量测误差影响定位精度。为提高估计精度,研究塔康中最佳线性无偏估计(best linear unbiased estimation,BLUE)滤波器的实现。建立地面站对目标的量测模型,并分析量测转换误差特性,推导出对应的BLUE滤波模型;针对目标从地面站上空过顶时出现无效量测的问题,通过对高程量测补偿的方法予以克服,解决传统算法在强非线性量测下误差较大的弊病。与经典方法的性能对比表明,改进算法有效地抑制了强非线性量测下的滤波发散,有很强的鲁棒性和实时性。

集中式BLUE滤波器在组网雷达中的应用

为解决组网雷达对目标跟踪中的量测非线性问题,提出基于最佳线性无偏估计器(BLUE)准则的融合滤波方法。建立以融合中心为原点的组网雷达对目标定位的量测方程,推导出极坐标系与球坐标系下跟踪目标的BLUE滤波模型。理论分析表明,集中式BLUE滤波架构在估计单个雷达量测转换误差统计特性的同时,还估计出雷达间量测转换误差的统计特性。因此,跟踪精度和置信度较分布式BLUE滤波方法有显著提高,计算量较其他算法也有明显优势。不同场景下的仿真分析证明:该方法在不同状态噪声水平下的表现优异,是一种很有竞争力的跟踪算法。

一种用于目标跟踪的UT-BLUE滤波方法

雷达机动目标跟踪问题中,通常目标运动模型可精确地在直角坐标系下建模,但大多数情形下模型是非线性的,同时在传感器坐标系下所获得目标量测又是直接可用的.通过将无迹变换与最优线性无偏滤波器有机结合,提出一种新的BLUE(Best Linear Unbiased Estimator)滤波算法,以便解决上述非线性跟踪问题.首先,该算法利用无迹变换对经由直角坐标系下非线性目标运动模型得到的目标状态及其协方差作出预测,然后在保持传感器坐标系(极坐标系)下所固有的量测误差的同时,直接对它们作出状态估计.在算法推导及Monte-Carlo仿真过程中,将新的BLUE滤波算法和EKF(Extended Kalman Filter)、UKF(Unscented Kalman Filter)滤波算法进行比较,结果表明新算法的有效性和适用性.

两参数Weibull分布基于BLUE的异常数据检验

文章针对样本数据服从两参数Weibull分布,定数截尾样本中出现异常数据的检验问题,定义了次序统计量的贡献率,利用参数σ的最佳线性无偏估计(BLUE)构造了检验统计量,并通过Monte-Carlo模拟得到了检验统计量分布的分位数,给出了异常数据的疑似个数,最后通过两个实例说明所给出的方法是可行的。

威布尔极值分布及其参数估计方法在储罐底板RBI计算中的应用

在常压储罐底板定量风险评估(RBI)过程中,底板腐蚀速率的选取会影响储罐整体风险等级的确定。基于威布尔极值分布理论,针对储罐边缘板,采用最佳线性无偏估计(BLUE)的方法计算了威布尔分布参数;针对储罐中幅板,采用简单线性无偏估计(GLUE)的方法计算了威布尔分布参数,随后通过线性回归理论分别计算了边缘板和中幅板的腐蚀速率,并将其赋予为腐蚀速率专家值对储罐底板进行了RBI计算。结果表明,威布尔极值分布理论计算可以科学合理地预估储罐底板腐蚀速率,边缘板的腐蚀速率为0.133 mm/a,中幅板的腐蚀速率为0.095 mm/a,与理论算法腐蚀速率的误差为3.6%,储罐底板风险等级为中高风险,在一定程度上解决了储罐RBI计算过程中腐蚀速率选取的难题,可为今后检验检测机构RBI的评估工作提供一定的技术支撑。

机动再入目标的FGPF-BLUE跟踪

机动再入目标的运动具有明显的非线性,其观测又往往在传感器坐标系下进行,构成强非线性的跟踪问题。为了克服扩展卡尔曼滤波和粒子滤波在跟踪精度和实时性方面的缺点,提出了一种新型的非线性跟踪算法。新型的FGPF-BLUE滤波将快速高斯粒子滤波的预测步骤与最优线性无偏估计的更新步骤相结合,是一种半蒙特卡罗滤波方法。建立了机动再入目标的动态模型,并分别应用扩展卡尔曼滤波、粒子滤波和FGPF-BLUE滤波实现了对该目标的跟踪。通过对各种滤波方法精度和消耗时间的对比,可知新方法的稳态性能优于其他两种算法,实时性优于粒子滤波。

一种基于BLUE的弹道目标跟踪滤波器

弹道目标跟踪问题中动态方程与观测方程皆为非线性,影响跟踪精度。使用最优线性无偏估计方法可以有效地解决观测的非线性问题,而蒙特卡罗滤波方法适用于目标状态的非线性估计。将快速高斯粒子滤波与最优线性估计方法作了有机结合,通过对典型的弹道目标跟踪模型进行仿真,发现新算法在精度与实时性上优于粒子滤波。

双侧截尾时双参数指数分布的BLUE

给出了双参指数分布在双侧截尾情况下未知参数σ和θ的最佳线性无偏估计（简记BLUE）和它们的方差．

含有随机效应的增长曲线模型回归系数阵的BLUE

应用Albert法给出了当设计阵、方差阵已知时,含有随机效应增长曲线模型回归系数阵B的可估函数KBL′(K,L均为已知的矩阵)方差矩阵在非负定意义下达到最小的最佳线性无偏估计,并证明了估计的优良性.

一般岭估计与BLUE的一个新的相对效率

考虑了Gauss-Markov模型中未知参数向量β的最佳线性无偏估计的改进问题,对一般岭估计与BLUE进行了比较,给出了一般岭估计与BLUE的一种新的相对效率e,并导出了e的下界.

Best Linear Unbiased Estimators of Location and Scale Ranked Set Parameters under Moving Extremes Sampling Design

In the current paper,the best linear unbiased estimators(BLUEs)of location and scale parameters from location-scale family will be respectively proposed in cases when one parameter is known and when both are unknown under moving extremes ranked set sampling(MERSS).Explicit mathematical expressions of these estimators and their variances are derived.Their relative efficiencies with respect to the minimum variance unbiased estimators(MVUEs)under simple random sampling(SRS)are compared for the cases of some usual distributions.The numerical results show that the BLUEs under MERSS are significantly more efficient than the MVUEs under SRS.

一类多元回归模型参数的优于BLUE的估计

本文考虑多元线性回归模型i=1,2,B_i 未知在一定的条件下,得到参数的优于 BLUE 的估计。

引入斜距和高程预测的BLUE跟踪方法

最佳线性无偏估计(BLUE:Best Linear Unbiased Estimation)算法用于目标跟踪时,受斜距、高程参量间的"共线"效应影响,对近程目标估计误差会增大甚至发散。针对此问题,在量测转换模型中引入斜距、高程预测,构建斜距、高程参量有偏估计,抑制"共线"效应。基于非线性参数误差最小准则推导斜距、高程估计的权值和偏置,建立基于非线性观测和状态预测融合估计的量测转换模型。基于该模型的BLUE算法能更精确的捕捉转换量测误差特性,以较小计算代价获得性能提升,数值仿真鲁棒性好,有很好应用前景。