Estimation of random errors for lidar based on noise scale factor

Estimation of random errors, which are due to shot noise of photomultiplier tube(PMT) or avalanche photodiode(APD) detectors, is very necessary in lidar observation. Due to the Poisson distribution of incident electrons, there still exists a proportional relationship between standard deviation and square root of its mean value. Based on this relationship,noise scale factor(NSF) is introduced into the estimation, which only needs a single data sample. This method overcomes the distractions of atmospheric fluctuations during calculation of random errors. The results show that this method is feasible and reliable.

EFFECTS OF RANDOM ERRORS ON THE SIDELOBE FOR THE LINEAR ARRAY

The effects of statistically dependent random errors on the sidelobe are analyzed for the linear array. It is shown that the random errors cause a rise in the sidelobe level. The simple formulas can also be obtained for the case of independent random errors.

TESTING OF CORRELATION AND HETEROSCEDASTICITY IN NONLINEAR REGRESSION MODELS WITH DBL(p,q,1) RANDOM ERRORS

Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity （Kantz and Schreiber （1997））. Tsai （1986） introduced a composite test for autocorrelation and heteroscedasticity in linear models with AR（1） errors. Liu （2003） introduced a composite test for correlation and heteroscedasticity in nonlinear models with DBL（p, 0, 1） errors. Therefore, the important problems in regression model axe detections of bilinearity, correlation and heteroscedasticity. In this article, the authors discuss more general case of nonlinear models with DBL（p, q, 1） random errors by score test. Several statistics for the test of bilinearity, correlation, and heteroscedasticity are obtained, and expressed in simple matrix formulas. The results of regression models with linear errors are extended to those with bilinear errors. The simulation study is carried out to investigate the powers of the test statistics. All results of this article extend and develop results of Tsai （1986）, Wei, et al （1995）, and Liu, et al （2003）.

Effect of random phase error and baseline roll angle error on eddy identification by interferometric imaging altimeter

To achieve better observation for sea surface,a new generation of wide-swath interferometric altimeter satellites is proposed.Before satellite launch,it is particularly important to study the data processing methods and carry out the detailed error analysis of ocean satellites,because it is directly related to the ultimate ability of satellites to capture ocean information.For this purpose,ocean eddies are considered a specific case of ocean signals,and it can cause significant changes in sea surface elevation.It is suitable for theoretical simulation of the sea surface and systematic simulation of the altimeter.We analyzed the impacts of random error and baseline error on the sea surface and ocean signals and proposed a combined strategy of low-pass filtering,empirical orthogonal function(EOF)decomposition,and linear fitting to remove the errors.Through this strategy,sea surface anomalies caused by errors were considerably improved,and the capability of satellite for capturing ocean information was enhanced.Notably,we found that the baseline error in sea surface height data was likely to cause inaccuracy in eddy boundary detection,as well as false eddy detection.These abnormalities could be prevented for"clean"sea surface height after the errors removal.

ON DESIGN METHOD OF THE PRECISION CAM PROFILE WITH RANDOM PROCESSING ERRORS

Based on probability and statistic, a design method of precision cam profileconcerning the influence of random processing errors is advanced. Combining the design with theprocess, which can be used to predict that cam profiles will be successfully processed or not in thedesign stage, design of the cam can be done by balancing the economization and reliability. Inaddition, an fuzzy deduction method based on Bayers formula is advanced to estimate processingreasonable of the designed precision cam profile, and it take few samples.

Error Threshold of Fully Random Eigen Model

Species evolution is essentially a random process of interaction between biological populations and their environ- ments. As a result, some physical parameters in evolution models are subject to statistical fluctuations. In this work, two important parameters in the Eigen model, the fitness and mutation rate, are treated as Gaassian dis- tributed random variables simultaneously to examine the property of the error threshold. Numerical simulation results show that the error threshold in the fully random model appears as a crossover region instead of a phase transition point, and ＆s the fluctuation strength increases the crossover region becomes smoother and smoother. Furthermore, it is shown that the randomization of the mutation rate plays a dominant role in changing the error threshold in the fully random model, which is consistent with the existing experimental data. The implication of the threshold change due to the randomization for antiviral strategies is discussed.

A Radiotherapy Treatment Margin Formula When Systematic Positioning Errors are Relatively Small Compared to Random Positioning Errors: A First-Order Approximation

A radiotherapy treatment margin formula has been analytically derived when a standard deviation (SD) of systematic positioning errors Ʃis relatively small compared to an SD of random positioning errors σ. The margin formula for 0 ≤ Ʃ≤ σwas calculated by linearly interpolating two boundaries at Ʃ= 0 and Ʃ= σ, assuming that the van Herk margin approximation of k1Ʃ+ k2σis valid at Ʃ= σ. It was shown that a margin formula for 0 ≤ Ʃ≤ σmay be approximated by k1σ+ k2Ʃ, leading to a more general form of k1 max(Ʃ,σ) + k2 min(Ʃ,σ) which is a piecewise linear approximation for any values of Ʃand σ.

THE APPLICATION OF RANDOM METRIC SPACE IN ERROR ESTIMATE

Throughout this paper,let(Ω,ι,μ)be a probability space,D the collection of allleft-continuous distribution functions,and D^+={F(0)=0|F∈D},and L(Ω)the collec-tion of all random variables which is a.s.finite on Ω,and L^+={ξ≥0 a.s.|ξ∈L(Ω)}.For random metric (normed) spaces,see [1]or[2].Theorem 1 Let(M,d)be a complete metric space f:M→M,a contract mappingwith contract coefficient α∈[0,1),L(Ω,m)the collection of all M-valued random vari-

THE LAW OF THE ITERATED LOGARITHM OF RANDOM WEIGHTING APPROXIMATION FOR MEAN ERROR──NON．I．I．D．SITUATION

For the dislribulion if mean error under independent but not identicallydislribuled conditions. its approximating dislribution whose precision reachO is obtained.

Effects of systematic phase errors on optimized quantum random-walk search algorithm

This study investigates the effects of systematic errors in phase inversions on the success rate and number of iterations in the optimized quantum random-walk search algorithm. Using the geometric description of this algorithm, a model of the algorithm with phase errors is established, and the relationship between the success rate of the algorithm, the database size, the number of iterations, and the phase error is determined. For a given database size, we obtain both the maximum success rate of the algorithm and the required number of iterations when phase errors are present in the algorithm. Analyses and numerical simulations show that the optimized quantum random-walk search algorithm is more robust against phase errors than Grover＇s algorithm.

USING ERROR-CORRECTING ENCODERS TO DESIGN LOCAL-RANDOM SEQUENCE GENERATORS

This paper proved the statement that a good linear block encoder is in fact a good local-random sequence generator. Furthermore, this statement discovers the deep relationship between the error-correcting coding theory and the modern cryptography.

全状态约束下长行程混联机器人投影迭代鲁棒控制算法

针对全状态约束下的长行程混联机器人系统鲁棒性较差提出了基于自适应学习神经网络和等效误差函数的投影迭代鲁棒控制算法。基于自适应学习神经网络逼近未知的非线性项,提出投影迭代鲁棒控制算法,更新网络权值并估计逼近误差和随机外部扰动的未知上界;构造用于抵消初始时刻随机变化扩展误差的时变边界层,设计基于时变边界层和扩展误差的等效误差函数作为迭代控制器的主要控制变量以克服随机初始误差满足相同初始条件;在控制器设计中引入正切型障碍Lyapunov函数,确保系统状态在预定范围内。仿真实验结果证明了该方法的有效性,可在全状态约束下实现高精度强鲁棒性的轨迹跟踪。

随机误差传递的高空气象秒级数据快速校验方法

【目的】为保证气象预报结果的准确性。【方法】提出了考虑随机误差传递的高空气象秒级数据快速校验方法。对高空气象秒级数据进行预处理,包括数据中心化处理、特征点提取。数据中心化的处理方式为实施各组数据的加权平均处理,获得新的数据组作为基准数据;特征点提取使用的处理方法为遍历法,需要遍历全部数据点。考虑数据获取过程中的随机误差传递情况,对预处理后的高空气象秒级数据实施空间一致性检查、内部一致性检查。应用集合经验模态分解EEMD(Ensemble Empirical Mode Decomposition)算法与对比源反演CSI(Contrast Source Inversion)算法构建基于CSI-EEMD的高空气象秒级数据快速校验模型,实施数据得到快速校验。【结果】测试结果表明,该方法能够实现4个气象站数据的快速校验,校验结果的均方根误差与平均绝对误差均低于0.1,实现了数据的质量控制。【结论】将该方法应用于气象站数据校验中,可以实现精准度的数据校验,具有一定的实用价值。

一种光学陀螺随机误差在线补偿方法

光学陀螺随机误差在线补偿方法通常先离线建立随机误差模型,再利用Kalman滤波在线补偿随机误差。受陀螺仪自身性能稳定性以及外界环境的影响,离线建立的随机误差模型应用于在线补偿时会出现偏差;外界环境变化导致量测噪声统计特性具有时变性,不满足Kalman滤波必须已知噪声先验统计的要求,这2个因素均会降低随机误差的在线估计精度。提出一种基于自适应滤波的随机误差在线补偿方法。通过引入虚拟噪声,将随机误差模型偏差的影响归结为时变虚拟系统噪声;进而利用渐消记忆时变噪声估值器,对虚拟系统噪声和量测噪声的统计特性进行估计与修正,消除随机误差模型偏差及量测噪声时变的影响。试验结果表明,所提方法可以实现对随机误差的高精度在线补偿,具有一定工程应用价值。

基于红外传感器的多无人机对高速飞行器协同定位跟踪方法综述

为了实现高精度远距离稳定定位与跟踪,多无人机搭载红外传感器对高速飞行器的定位跟踪方法成为研究热点。就此背景进行了综合评述,以相关技术难点及关键技术为切入点,分析了瞬时定位和动态跟踪问题的研究现状和发展方向。在瞬时协同定位方面,首先对目标瞬时定位的算法进行了分析对比,然后对造成定位误差的影响因素进行分析并探讨了误差补偿方法,最后针对传感器资源优化,讨论了能够使计算结果最优的传感器分布阵型。在动态跟踪方面,首先探讨了时间不同步问题对目标跟踪的影响及补偿方法,然后对目标运动学建模的问题进行了分析,最后对最优估计问题中各种滤波算法的应用进行对比。综述表明,无人机搭载红外传感器对高速飞行器进行定位和跟踪,存在误差来源多、相对运动速度快的技术难点,需要特别关注定位算法、误差来源及最优估计算法的影响,以尽可能实现稳定跟踪。

基于赋权偏差传递网络的6Dof-RUS并联机构随机误差分析

对于已经完成加工装配高精度并联机构,当出现使用精度无法满足设计要求时,需要对产生误差的源头进行追溯,只修复部分构件提高机构精度。以单点激光测距仪检测方法与六条由转动副(R)虎克铰链(U)球面副(S)构成单支链的6自由度并联机构(6Dof-RUS)随机误差模型之间的映射为研究内容。首先构建了6Dof-RUS并联机构赋权偏差传递网络,其次建立了相机坐标系中光斑与平面约束之间的映射方程,通过结构误差雅可比矩阵将结构参数误差映射为可观测的动平台位姿误差。最后进行了实验验证,实验结果表明,在有限次测量次数的情况下可快速对随机误差进行溯源,验证了所提方法的有效性与可靠性,对其他拓扑构型并联机构精度综合具有一定借鉴意义。

基于三维全参数反演的煤矿采空区形变提取方法研究

准确提取地表形变信息对于预防和控制煤矿开采导致的地质灾害至关重要。以山东郭屯煤矿某工作面为例,首先获取了工作面开采时间段内(2017年7月31日至2018年5月3日)的18景Sen-tinel-1A卫星影像,基于SBAS-InSAR技术处理得到工作面采空区地表形变。在InSAR观测数据的驱动下,通过推导概率积分法与SBAS-InSAR视线向形变三维参数之间的函数映射关系,提出了一种基于随机误差消除遗传算法的三维全参数反演模型。基于该方法,准确反演了研究区地表沉降参数,通过与现场经验值对比,各参数的偏差均小于3%,拟合精度较高。最后,基于反演参数与概率积分法获得了采空区全盆地沉降形变信息,其中A测线与F测线的均方根误差分别为0.083 m和0.102 m,平均绝对误差分别为0.068 m和0.089 m,预计结果与实测水准测量数据高度一致,表明所提出的三维全参数反演模型能够以低成本的方式有效获取煤矿采空区全盆地沉降信息。

基于ECOC平衡随机森林的雷达降水粒子分类

针对数据不平衡情况下的降水粒子分类问题,提出了一种基于纠错输出码(error correcting output code,ECOC)平衡随机森林的双偏振气象雷达降水粒子分类方法。首先,将多类别降水粒子数据集编码为多个二分类数据集;然后,对二分类数据集进行有放回的平衡重采样,构建多棵分类回归树;最后,利用所有的分类回归树联合进行降水粒子分类。对实测数据的处理结果表明,所提方法能够在保证总体准确率较高的情况下,大幅提高少数类的分类效果。

利用星载激光雷达数据校正森林区DEM误差

在植被区,通过光学遥感或InSAR技术生产的DEM产品不能反映真实林下地形高度。森林区DEM误差主要是植被高引起的系统偏差,植被覆盖度和地形是森林区DEM的主要误差来源。新一代星载激光雷达可以提供大量高精度林下地形控制点产品,为森林区DEM误差的纠正提供了新的契机。鉴于此,文章提出基于机器学习框架下顾及植被覆盖及地形因素的林区DEM误差校正方法。首先,获取高精度星载激光雷达地形控制点与DEM的地形残差;其次,利用光学遥感数据、SAR遥感数据及DEM产品数据计算与植被覆盖和地形有关的特征参数;最后,联合这些特征参数与获取的地形残差点分别建立不同类型DEM产品误差校正模型。选取位于美国田纳西州和北卡罗来纳州交界处的山地林区作为本研究的实验区。研究结果表明,相对原始DEM,校正高程误差后的DEM精度提升超过40%,有效校正了林区DEM误差。