Navigation-Optimal-Guidance for Precision Planetary Landing by Convexification of Estimated-Error-Variance of Navigation

2019 ◽  
Vol 67 (3) ◽  
pp. 81-92
Author(s):  
Hisaaki Arai ◽  
Shinichiro Sakai
Keyword(s):  
Error Variance ◽  
Optimal Guidance ◽  
Planetary Landing ◽  
Estimated Error

scholarly journals Estimation of Error Variance-Covariance Parameters Using Multivariate Geographically Weighted Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-5 ◽  
Author(s):  
Sri Harini
Keyword(s):  
Statistical Inference ◽  
Geographically Weighted Regression ◽  
Weighting Function ◽  
Likelihood Estimation ◽  
Error Variance ◽  
Least Square ◽  
Weighted Regression ◽  
Inference Procedure ◽  
Geographically Weighted Regression Model ◽  
Estimated Error

The Multivariate Geographically Weighted Regression (MGWR) model is a development of the Geographically Weighted Regression (GWR) model that takes into account spatial heterogeneity and autocorrelation error factors that are localized at each observation location. The MGWR model is assumed to be an error vector ε that distributed as a multivariate normally with zero vector mean and variance-covariance matrix Σ at each location ui,vi, which Σ is sized qxq for samples at the i-location. In this study, the estimated error variance-covariance parameters is obtained from the MGWR model using Maximum Likelihood Estimation (MLE) and Weighted Least Square (WLS) methods. The selection of the WLS method is based on the weighting function measured from the standard deviation of the distance vector between one observation location and another observation location. This test uses a statistical inference procedure by reducing the MGWR model equation so that the estimated error variance-covariance parameters meet the characteristics of unbiased. This study also provides researchers with an understanding of statistical inference procedures.

scholarly journals Estimating observation and model error variances using multiple data sets

2018 ◽  
Vol 11 (7) ◽  
pp. 4239-4260 ◽  
Author(s):  
Richard Anthes ◽  
Therese Rieckh
Keyword(s):  
Error Variance ◽  
Specific Humidity ◽  
Data Sets ◽  
Data Set ◽  
Multiple Data ◽  
Gfs Model ◽  
Multiple Data Sets ◽  
Using Data ◽  
The Tropics ◽  
Estimated Error

Abstract. In this paper we show how multiple data sets, including observations and models, can be combined using the “three-cornered hat” (3CH) method to estimate vertical profiles of the errors of each system. Using data from 2007, we estimate the error variances of radio occultation (RO), radiosondes, ERA-Interim, and Global Forecast System (GFS) model data sets at four radiosonde locations in the tropics and subtropics. A key assumption is the neglect of error covariances among the different data sets, and we examine the consequences of this assumption on the resulting error estimates. Our results show that different combinations of the four data sets yield similar relative and specific humidity, temperature, and refractivity error variance profiles at the four stations, and these estimates are consistent with previous estimates where available. These results thus indicate that the correlations of the errors among all data sets are small and the 3CH method yields realistic error variance profiles. The estimated error variances of the ERA-Interim data set are smallest, a reasonable result considering the excellent model and data assimilation system and assimilation of high-quality observations. For the four locations studied, RO has smaller error variances than radiosondes, in agreement with previous studies. Part of the larger error variance of the radiosondes is associated with representativeness differences because radiosondes are point measurements, while the other data sets represent horizontal averages over scales of ∼ 100 km.

The effects of some variables on the components of reproduction rate in the Merino

1969 ◽  
Vol 20 (3) ◽  
pp. 549 ◽  
Author(s):  
Haas HJ De ◽  
AA Dunlop
Keyword(s):  
Body Weight ◽  
Error Variance ◽  
The Other ◽  
Multiple Births ◽  
Multiple Birth ◽  
Reproduction Rate ◽  
Components Of Variance ◽  
Estimated Error ◽  
Main Effects ◽  
Single Birth

Reproductive records covering 4855 ewe-years coming from five strains of Merino ewe run at three locations over 5 years were classified into those which resulted in (a) failure to lamb, (b) a single birth, or (c) a multiple birth. Age of ewe was included as a further classification, while pre-mating body weight was considered as a covariate. The data were analysed by least squares procedures. In all analyses in which components of variance were estimated, error variance constituted more than 90% of the total. Of the main effects, those due to age were generally largest, particularly where they related to the proportion of dry ewes and multiple births, though year effects on the proportion of dry ewes ranged up to 0.10. The effects of pre-mating body weight on lambing performance were small though real, the largest being an increase of 0.37% of multiple births per pound increase in body weight. First order interactions were generally small, the most prominent being location x strain, location x age, and location x year. The third of these had the largest effects and accounted for more of the variance. This was particularly so in the proportions of dry ewes and single births. Location x age interactions, on the other hand, were more prominent in affecting the proportion of multiple births, where the increase with age was much less marked at one location than at the other two. Strain x location interactions were not large enough to suggest any marked adaptation of strains to particular locations in these mutually dependent traits.

scholarly journals A New Method for Temperature Spatial Interpolation Based on Sparse Historical Stations

2018 ◽  
Vol 31 (5) ◽  
pp. 1757-1770 ◽  
Author(s):  
Chengdong Xu ◽  
Jinfeng Wang ◽  
Qingxiang Li
Keyword(s):  
Error Variance ◽  
Absolute Error ◽  
Historical Period ◽  
Climate Change Research ◽  
Air Temperatures ◽  
Point Interpolation ◽  
Historical Temperature ◽  
Leave One Out ◽  
Variance Estimates ◽  
Estimated Error

Long-term grid historical temperature datasets are the foundation of climate change research. Datasets developed by traditional interpolation methods usually contain data for a period of less than 50 yr, with a relatively low spatial resolution owing to the sparse distribution of stations in the historical period. In this study, the point interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (P-BSHADE) method has been used to interpolate 1-km grids of monthly surface air temperatures in the historical period of 1900–50 in China. The method can be used to remedy the station bias resulting from sparse coverage, and it considers the characteristics of spatial autocorrelation and nonhomogeneity of the temperature distribution to obtain unbiased and minimum error variance estimates. The results have been compared with those from widely used methods such as kriging, inverse distance weighting (IDW), and a combined spline with kriging (TPS-KRG) method, both theoretically and empirically. The leave-one-out cross-validation method using a real dataset was implemented. The root-mean-square error (RMSE) [mean absolute error (MAE)] for P-BSHADE is 0.98°C (0.75°C), while those for TPS-KRG, kriging, and IDW are 1.46° (1.07°), 2.23° (1.51°), and 2.64°C (1.85°C), respectively. The results of validation using a simulated dataset also present the smallest error for P-BSHADE, demonstrating its empirical superiority. In addition to its empirical superiority, the method also can produce a map of the estimated error variance, representing the uncertainty of estimation.

Optimal Guidance for Planetary Landing in Hazardous Terrains

2020 ◽  
Vol 56 (4) ◽  
pp. 2896-2909 ◽  
Author(s):  
Chengchao Bai ◽  
Jifeng Guo ◽  
Hongxing Zheng
Keyword(s):  
Optimal Guidance ◽  
Planetary Landing

An optimal guidance law for planetary landing

1997 ◽  
Author(s):  
Christopher D'Souza ◽  
Christopher D'Souza
Keyword(s):  
Guidance Law ◽  
Optimal Guidance ◽  
Planetary Landing

Absolute inner-shell cross section determination for EELS analysis

1988 ◽  
Vol 46 ◽  
pp. 534-535
Author(s):  
P.A. Crozier
Keyword(s):  
Cross Section ◽  
Cross Sections ◽  
Absolute Cross Section ◽  
Specimen Thickness ◽  
Mass Thickness ◽  
Scattering Cross ◽  
Before And After ◽  
Estimated Error ◽  
Scattering Cross Sections ◽  
Electron Microscopist

Absolute inelastic scattering cross sections or mean free paths are often used in EELS analysis for determining elemental concentrations and specimen thickness. In most instances, theoretical values must be used because there have been few attempts to determine experimental scattering cross sections from solids under the conditions of interest to electron microscopist. In addition to providing data for spectral quantitation, absolute cross section measurements yields useful information on many of the approximations which are frequently involved in EELS analysis procedures. In this paper, experimental cross sections are presented for some inner-shell edges of Al, Cu, Ag and Au.Uniform thin films of the previously mentioned materials were prepared by vacuum evaporation onto microscope cover slips. The cover slips were weighed before and after evaporation to determine the mass thickness of the films. The estimated error in this method of determining mass thickness was ±7 x 107g/cm2. The films were floated off in water and mounted on Cu grids.

Measuring the Big Five in Self-Report and Other Ratings

2000 ◽  
Vol 16 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Claudio Barbaranelli ◽  
Gian Vittorio Caprara
Keyword(s):  
Big Five ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Error Variance ◽  
The Self ◽  
Self Report ◽  
Sources Of Information ◽  
Third Person ◽  
Fit Indexes ◽  
Response Modes

Summary: The aim of the study is to assess the construct validity of two different measures of the Big Five, matching two “response modes” (phrase-questionnaire and list of adjectives) and two sources of information or raters (self-report and other ratings). Two-hundred subjects, equally divided in males and females, were administered the self-report versions of the Big Five Questionnaire (BFQ) and the Big Five Observer (BFO), a list of bipolar pairs of adjectives ( Caprara, Barbaranelli, & Borgogni, 1993 , 1994 ). Every subject was rated by six acquaintances, then aggregated by means of the same instruments used for the self-report, but worded in a third-person format. The multitrait-multimethod matrix derived from these measures was then analyzed via Structural Equation Models according to the criteria proposed by Widaman (1985) , Marsh (1989) , and Bagozzi (1994) . In particular, four different models were compared. While the global fit indexes of the models were only moderate, convergent and discriminant validities were clearly supported, and method and error variance were moderate or low.

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