• Title/Summary/Keyword: Error covariance

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A FRAMEWORK TO UNDERSTAND THE ASYMPTOTIC PROPERTIES OF KRIGING AND SPLINES

  • Furrer Eva M.;Nychka Douglas W.
    • Journal of the Korean Statistical Society
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    • v.36 no.1
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    • pp.57-76
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    • 2007
  • Kriging is a nonparametric regression method used in geostatistics for estimating curves and surfaces for spatial data. It may come as a surprise that the Kriging estimator, normally derived as the best linear unbiased estimator, is also the solution of a particular variational problem. Thus, Kriging estimators can also be interpreted as generalized smoothing splines where the roughness penalty is determined by the covariance function of a spatial process. We build off the early work by Silverman (1982, 1984) and the analysis by Cox (1983, 1984), Messer (1991), Messer and Goldstein (1993) and others and develop an equivalent kernel interpretation of geostatistical estimators. Given this connection we show how a given covariance function influences the bias and variance of the Kriging estimate as well as the mean squared prediction error. Some specific asymptotic results are given in one dimension for Matern covariances that have as their limit cubic smoothing splines.

Position estimation of mobile robot using modified kalman filter (변형된 칼만 필터를 이용한 이동 로봇의 위치 추정)

  • Kang, Seon-Ho;Jung, Kyung-Kwon;Lee, Yong-Gu;Eom, Ki-Hwan
    • Proceedings of the IEEK Conference
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    • 2006.06a
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    • pp.1005-1006
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    • 2006
  • This paper proposes a method of position estimating through compensating the autonomous mobile robot's noise. Proposed method is that estimated position error by modified Kalman filter method using neural network. We use a neural network for measurement noise covariance and system noise covariance. In order to verify the effectiveness of the proposed method, we performed experiments for position estimation. The results show that convergence and position error is reduced than the Kalman filter method.

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Quantification of Angular Prediction Accuracy for Phased Array Radar Tracking (위상배열레이더 추적 각도예측의 정확도 정량화)

  • Hong, Sun-Mog
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.49 no.1
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    • pp.74-79
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    • 2012
  • Scalar quantification of the angular prediction error covariance matrix is considered for characterizing tracking performances in phased array radar tracking. Specifically, the maximum eigenvalue and the trace of the covariance matrix are examined in terms of consistency in parameterizing the probability of detection, taking antenna beam-pointing losses into account, and it is shown numerically that the latter is more consistent.

UKF Localization of a Mobile Robot in an Indoor Environment and Performance Evaluation (실내 이동로봇의 UKF 위치 추정 및 성능 평가)

  • Han, Jun Hee;Ko, Nak Yong
    • Journal of the Korean Institute of Intelligent Systems
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    • v.25 no.4
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    • pp.361-368
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    • 2015
  • This paper reports an unscented Kalman filter approach for localization of a mobile robot in an indoor environment. The method proposes a new model of measurement uncertainty which adjusts the error covariance according to the measured distance. The method also uses non-zero off diagonal values in error covariance matrices of motion uncertainty and measurement uncertainty. The method is tested through experiments in an indoor environment of 100*40 m working space using a differential drive robot which uses Laser range finder as an exteroceptive sensor. The results compare the localization performance of the proposed method with the conventional method which doesn't use adaptive measurement uncertainty model. Also, the experiment verifies the improvement due to non-zero off diagonal elements in covariance matrices. This paper contributes to implementing and evaluating a practical UKF approach for mobile robot localization.

Wideband adaptive beamforming method using subarrays in acoustic vector sensor linear array (부배열을 이용한 음향벡터센서 선배열의 광대역 적응빔형성기법)

  • Kim, Jeong-Soo;Kim, Chang-Jin;Lee, Young-Ju
    • The Journal of the Acoustical Society of Korea
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    • v.35 no.5
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    • pp.395-402
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    • 2016
  • In this paper, a wideband adaptive beamforming approach for an acoustic vector sensor linear array is presented. It is a very important issue to estimate the stable covariance matrix for adaptive beamforming. In the conventional wideband adaptive beamforming based on coherent signal-subspace (CSS) processing, the error of bearing estimates is resulted from the focusing matrix estimation and the large number of data snapshot is necessary. To alleviate the estimation error and snapshot deficiency in estimating covariance matrix, the steered covariance matrix method in the pressure sensor is extended to the vector sensor array, and the subarray technique is incorporated. By this technique, more accurate azimuth estimates and a stable covariance matrix can be obtained with a small number of data snapshot. Through simulation, the azimuth estimation performance of the proposed beamforming method and a wideband adaptive beamforming based on CSS processing are assessed.

Estimation of Spatial Coherency Functions for Kriging of Spatial Data (공간데이터 크리깅 적용을 위한 공간상관함수 추정)

  • Bae, Tae-Suk
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.34 no.1
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    • pp.91-98
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    • 2016
  • In order to apply Kriging methods for geostatistics of spatial data, an estimation of spatial coherency functions is required priorly based on the spatial distance between measurement points. In the study, the typical coherency functions, such as semi-variogram, homeogram, and covariance function, were estimated using the national geoid model. The test area consisting of 2°×2° and the Unified Control Points (UCPs) within the area were chosen as sampling measurements of the geoid. Based on the distance between the control points, a total of 100 sampling points were grouped into distinct pairs and assigned into a bin. Empirical values, which were calculated with each of the spatial coherency functions, resulted out as a wave model of a semi-variogram for the best quality of fit. Both of homeogram and covariance functions were better fitted into the exponential model. In the future, the methods of various Kriging and the functions of estimated spatial coherency need to be studied to verify the prediction accuracy and to calculate the Mean Squared Prediction Error (MSPE).

Design of a vehicle navigation system using the federated kalman filter (연합형 칼만 필터를 이용한 차량항법시스템의 설계)

  • 김진원;지규인;이장규
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.1348-1351
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    • 1997
  • The federated Kaman filter(FKF) is being widely used in many multisensor navigatiion systems. It is know that the FKF has advantages of simplicity and fault-tolerance over other decentralized filter techniques. In this paper, optimal and suboptimal FKF configuratiions are mentioned and a covariance analysis technique for the suboptimal FKF is newly presented. The suboptimal FKF configuration, known as No-reset(NR) mode, has better fault tolerance capability than the optimal FKF coniguratioin. In the suggested technique, a suboptimal fusion process of FKF is considered a swell as suboptimal gains of local filters. An upper boun of error covariance for suboptimal FKF is derived. Also, it is mathematically shown that this bound is smaller thanexisting bound in the literatrue. A vehicle-navigaion system is designed using the FKF. In thissystem, a map constraing equation is introduced and used as a measurement equatioin of Kalman filter. Performance analysis is done by the suggested covariance analysis techniques.

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Fast Speaker Adaptation Using Sub-Stream Based Eigenvoice (Sub-Stream 기반의 Eigenvoice를 이용한 고속 화자적응)

  • Song, Hwa-Jeon;Lee, Jong-Seok;Kim, Hyung-Soon
    • MALSORI
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    • v.55
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    • pp.93-102
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    • 2005
  • In this paper, sub-stream based eigenvoice method is proposed to overcome the weak points of conventional eigenvoice and dimensional eigenvoice. In the proposed method, sub-streams are automatically constructed by the statistical clustering analysis that uses the correlation information between dimensions. To obtain the reliable distance matrix from covariance matrix for dividing into optimal sub-streams, MAP adaptation technique is employed to the covariance matrix of training data and the sample covariance of adaptation data. According to our experiments, the proposed method shows $41\%$ error rate reduction when the number of adaptation data is 50.

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A Simplified Li-ion Battery SOC Estimating Method

  • Zhang, Xiaoqiang;Wang, Xiaocheng;Zhang, Weiping;Lei, Geyang
    • Transactions on Electrical and Electronic Materials
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    • v.17 no.1
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    • pp.13-17
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    • 2016
  • The ampere-hour integral method and the open circuit voltage method are integrated via the extended Kalman filter method so as to overcome insufficiencies of the ampere-hour integral method and the open circuit voltage method for estimating battery SOC. The process noise covariance and the measurement noise covariance of the extended Kalman filter method are simplified based on the Thevenin equivalent circuit model, with a proposed simplified SOC estimating method. Verification of DST experiments indicated that the battery SOC estimating method is simple and feasible, and the estimated SOC error is no larger than 2%.

Bootstrap confidence intervals for classification error rate in circular models when a block of observations is missing

  • Chung, Hie-Choon;Han, Chien-Pai
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.4
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    • pp.757-764
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    • 2009
  • In discriminant analysis, we consider a special pattern which contains a block of missing observations. We assume that the two populations are equally likely and the costs of misclassification are equal. In this situation, we consider the bootstrap confidence intervals of the error rate in the circular models when the covariance matrices are equal and not equal.

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