SEARCH WITHIN CONTENT
Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 2, Pages 1,180-1,202, DOI: https://doi.org/10.21307/ijssis-2017-802
License : (CC BY-NC-ND 4.0)
Published Online: 20-November-2017
Measure error of photoelectric theodolite would influence result precision when tracking flight target. This paper researches error problem of testing. It analyses all the causes of error forming, divides them to several sorts: system error, random error and outlier error, then provides resolution method to each sort. Especially for random error, it builds an error model, analyses the properties of unbiased, equal variance and uncorrelated, conducts best error estimate, discuss the relationship between choosing and effect of kernel function and smooth parameter. While it researches measure theory of coplanar intersection and dis-coplanar intersection of photoelectric theodolite, derives a series of measure formulas, builds random error model respectively, and analyses the relationship of effect actors. By comparing simulation of model with experiment measurement, the result shows the error model and processing method is correct.
Yao Q, Brockwell P, “Gaussian maximum likelihood estimation for ARMA models II: spatial processes”, Bernoulli, Vol.12, No.3, 2006, pp. 403-429.
Krishna, Ramuhalli, “Improved pointing accuracy using high-precision theodolite measurements”, SPIE, Vol.2818, 1998, No.1, pp.199-209.
Wang Jianli, Chen Tao, Qi Chen qne Fang Zhonghua, “Method to improve the capability of electro-optical theodolite to track fast-moving target”, SPIE, Vol. 4564, No.1, 2003, pp. 238-244.
LIU Chanlao, “The research on dynamic tracking and measurement and its computer simulation in shooting range”, Vol.13, No.3, 2001, pp. 455-479.  H.J.Tiziani, “High precision optical measurement methods”, SPIE, Vo1.2248, No.1, 1996, pp.2-18.  C. J. Stone, “Consistent nonparametric regression”, The Annals of Statistics, Vo1.17, No.5, 1977, pp.595-645.  KlEIN L A, “Sensor and data fusion concepts and applications”, SPIE, Optical Engineering Press, Vo1.1848, No.1, 1993, pp.364-389.  W.S.Cleveland, “Robust locally weighted regression and smoothing scatter plots”, Journal of American Statistical Association, Vo1.74, No.2, 1979, pp.829-836. Hallin M, Lu Z, Tran L.T, “Local linear spatial regression”, Annals of Statistics, Vol.32, No.6, 2004, pp.356-378. Lee L F, “Consistency and efficiency of least squares estimation for mixed regressive”, Spatial Autoregressive Models, Econometric Theory, Vol.18, No.1, 2002, pp.252–277. E. Masry, J. Fan, “Local polynomial estimation of regression functions for mixing processes”, Scandinavian Journal of Statistics, Vol.24, No.3, 1997, pp.165-179.
J.Fan,, I.Gijbels, “Local polynomial modelling and its applications”, Chapman & Hall. London, 1996, pp.196-210. H.Shen, L.Brown, “Nonparametric modelling for time-varying customer service times at a bank call center”, Appl Stoch Models Bus Ind, Vol.22, No.6, 2006, pp.297-311.
 Cliff A, Ord J K, “Spatial processes: models & applications”, London: Pion, pp.1-131, January 1981. L.Wang, L. Brown, T.Cai, M.Levine, “Effect of mean on variance function estimation in nonparametric regression. Technical report”, Dept. Statistics, Univ. Pennsylvania, Available at:www-stat. wharton. upenn. edu/tcai/paper/html/Variance-Estimation.Html, 2006.
P.Hall, R.Carroll, “Variance function estimation in regression: the effect of estimating the mean”, J. Roy. Statist. Soc. Ser. B, Vol.51, 2006, No.2, pp.3-14. P.Hall, J.Kay, D.Titterington, “Asymptotically optimal difference-based estimation of variance in nonparametric regression”, Biometrika, Vol.77, No.3, 2006, pp.521-528.
 S.C.Mukhopadhyay, F.P. Dawson, M. Iwahara and S. Yamada, “A Novel Compact Magnetic Current Limiter for Three Phase Applications”, IEEE Transactions on Magnetics, Vol. 36, No. 5, pp. 3568-3570, September 2000. Delaigle A,Fan J,Carroll R J,“A design-adaptive local polynomial estimator for the errors-in-variables problem”, Amer.Statist.Assoc,Vol.104 , No.1, 2009, pp. 348–359. Comte F, Taupin M L, “Nonparametric estimation of the regression function in an errors-in-variables model”, Statist.Sinica, Vol.17, No.3, 2007, pp.1065–1090.
Meister Alexander, “Deconvolution problems in nonparametric statistics”, Lecture Notes in Statistics, Springer, Berlin, Heidelberg, 2009, pp.5-138.  S.C.Mukhopadhyay, K. Chomsuwan, C. Gooneratne and S. Yamada, “A Novel Needle-Type SV-GMR Sensor for Biomedical Applications”, IEEE Sensors Journal, Vol. 7, No. 3, pp. 401-408, March 2007. J. Fan, T. Gasser, I. Gijbels, M. Brockmann and J. Engel, “Local polynomial fitting: optimal kernel and asymptotic minimax efficiency”, Annals of the Institute of Statistical Mathematics, Vol.49, No.2, 1996, pp.79-99. Masry E., “Deconvolving multivariate kernel density estimates from contaminated associated observations”, IEEE Trans. Inform. Theory, Vol.49. No.11, 2003, pp.2941–2952. Wu W B, Mielniczuk J, “Kernel density estimation for linear processes”, Ann. Statist.,Vol.30, No.5, 2002, pp.1441–1459. Fan J, Truong Y K, “Nonparametric regression with errors in variables”, Ann. Statist.,Vol.21, No.4, 1993, pp.1900–1925.  K. Muthumeenakshi and S. Radha, “Optimal techniques for sensing error minimization with improved energy detection in cognitive radios”, International Journal on Smart Sensing and Intelligent Systems, Vol 7, No. 4, December 2014, pp. 2014-2034.
 S. Yamada, K. Chomsuwan, S.C.Mukhopadhyay, M. Iwahara, M. Kakikawa and I. Nagano, “Detection of Magnetic Fluid Volume Density with a GMR Sensor”, Journal of Magnetics Society of Japan, Vol. 31, No. 2, pp. 44-47, 2007. Mielniczuk J, Wu W B, “On random-design model with dependent errors”, Statist. Sinica.,Vol. 14, No.4, 2004, pp.1105–1126.
Kulik R., “Nonparametric deconvolution problem for dependent sequences”, Electron. J.Statist., Vol. 2, No.3, 2008, pp.722–740.  Biswajit Panja, Zachary Scott and Priyanka Meharia, “Security of wireless sensor networks for health monitoring helmets with anomaly detection using power analysis and probabilistic model”, International Journal on Smart Sensing and Intelligent Systems, Vol 8, No. 1, March 2015, pp. 561-580.