GAUSS KALMAN AND ADVANCES IN RECURSIVE PARAMETER ESTIMATION

models are assumed to be approximations of some “true” underlying unknown model(i.e. if all models may be misspecified). Of note is that Inoue and Kilian (2004) claimthat in-sample tests are more powerful than simulated out-of-sample variants thereof.Their findings are based on the exami[r]

Hindawi Publishing CorporationEURASIP Journal on Wireless Communications and NetworkingVolume 2009, Article ID 893751, 10 pagesdoi:10.1155/2009/893751Research ArticleAn Adaptive Channel Interpolator Based on Kalman Filter forLTE Uplink in High Doppler S pre ad EnvironmentsBahatt[r]

contributions in the area depend upon recent advances including results validating theuse of the bootstrap [see, e.g., Horowitz (2001)] and the invention of crucial tools fordealing with parameter estimation error [see, e.g., Ghysels and Hall (1990), Khmalad[r]

5.13bOutput of the speed velocity with va(V) vs df(m) ............................... 1545.14Flow chart of a semi-autonomous wheelchair control system, in whichintention estimation applied to estimate state including uuser and uauto 1555.15Head commands using a head sensor are r[r]

utilizes the analytical solution to manipulate the problem such that it can be reformulated into two optimization stages: the first stage to optimize for the modelparameters, keeping the variable estimates fixed at the results from previous iteration,and the second to optimize for the variabl[r]

In this chapter, we will present several nonnumerical recursive algorithms in this chapter. We will also discuss some criteria for deciding when to use recursion and when not to. All the recursive algorithms we provide in this chapter, other than those we use for explanation, are algorithms that sho[r]

Metabolomics has a great potential in the development of new biomarkers in cancer and it has experiment recent technical advances.

estimators that we are going to compare: κ-type (FC) estimator, ANOVA estimator,Gaussian likelihood estimator and the new estimator based on Cholesky decomoposition. Chapter 3 will carry the simulation studies to compare the performances of thesefour estimators. We will compare the bias, stan[r]

Hindawi Publishing CorporationEURASIP Journal on Advances in Signal ProcessingVolume 2007, Article ID 68985, 8 pagesdoi:10.1155/2007/68985Research ArticleLinear Motion Blur Parameter Estimation in Noisy ImagesUsing Fuzzy Sets and Power SpectrumMohsen Ebrahim[r]

Lin, C Y.; Peng, W C. & Tseng, Y C. (2006). Efficient In-network Moving Object Tracking in Wireless Sensor Network, IEEE Transactions on Mobile Computing, vol.5, no.8, pp.10441056. Liu, J.; Reich, J. & Zhao, F. (2003). Collaborative In-Network Processing for Tar[r]

nent than at the lower scales. Therefore, if the logarithmof both sides of (10)istaken,acurvelikebehaviorisob-served, instead of a straight line, as is illustrated in Figure 1where simulation results are given for flicker noise (γ= 1)and additive white noise (γ= 0). Here, the data lengt[r]

ProjectNNNLocationEmployeeskill-usedassigned-toTeorey.book Page 63 Saturday, July 16, 2005 12:57 PM64 CHAPTER 4 Requirements Analysis and Conceptual Data ModelingGlobal ER Schema A simple integration of the three views over the entity Employee definesresults in the global ER schema (di[r]

Foundation of China 10771118, the Natural Science Foundation of Shandong ProvinceZR2009AM011, and the Science Foundation of the Education Department of ShandongProvince, China J07yh05.References1 S. Hilger, “Analysis on measure chains—a unified approach to continuous and discret[r]

9GENERAL RECURSIVE MINIMUM-VARIANCE GROWING-MEMORYFILTER (BAYES AND KALMANFILTERS WITHOUT TARGETPROCESS NOISE)9.1 INTRODUCTIONIn Section 6.3 we developed a recursive least-squares growing memory-filter forthe case where the target trajectory is approximated by a polynomial. In[r]

class;itisthesecond-orderspectrum.HOSconsistofhigher-ordermomentspectra,whicharedefinedfordeterministicsignals,andcumulantspectra,whicharedefinedforrandomprocesses.Ingeneral,therearethreemotivationsbehindtheuseofHOSinsignalprocessing:(1)tosuppressGaussiannoiseofunknownmeanandvariance;(2)toreconstructt[r]

atiment Euler (A.202),Avenue Georges Lemaitre 4, B-1348 Leuven-Heverlee, BelgiumReceived 27 September 2007; Accepted 27 September 2007Copyright © 2007 Nicola Mastronardi et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, d[r]

class;itisthesecond-orderspectrum.HOSconsistofhigher-ordermomentspectra,whicharedefinedfordeterministicsignals,andcumulantspectra,whicharedefinedforrandomprocesses.Ingeneral,therearethreemotivationsbehindtheuseofHOSinsignalprocessing:(1)tosuppressGaussiannoiseofunknownmeanandvariance;(2)toreconstructt[r]

the enzyme to the most appropriate rate law. Thesekinetic rate laws are formulated with mathematical func-tions of metabolite concentration(s) and one or morekinetic parameters. In combination with the stoichiome-try of the metabolism, these kinetic rate laws define thefunction of the[r]

affects the grouping. Pascal-Cow and Pascal-Cat have a wideappearance variation and are difficult to make groups. Butthe direction of bodies and the texture roughly form groups.4.6. Comparison with Fergus’s Model. Because Fergus’s modelrequires high computation cost and does[r]

degrees of freedom(Hansen 1982, p. 1049). When the GMM method is selected, the value of the overidentifyingrestrictions test statistic, also known as Hansen’s J test statistic, and its associated number of degreesof freedom are reported together with the probability under the null hypothesis.[r]