Lp Estimate

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The Experts below are selected from a list of 5622 Experts worldwide ranked by ideXlab platform

Takayuki Abe - One of the best experts on this subject based on the ideXlab platform.

Yoshihiro Shibata - One of the best experts on this subject based on the ideXlab platform.

Sikora Adam - One of the best experts on this subject based on the ideXlab platform.

  • Gradient Estimates for heat kernels and harmonic functions
    'Elsevier BV', 2020
    Co-Authors: Coulhon Thierry, Jiang Renjin, Koskela Pekka, Sikora Adam
    Abstract:

    Let (X,d,μ) be a doubling metric measure space endowed with a Dirichlet form E deriving from a “carré du champ”. Assume that (X,d,μ,E) supports a scale-invariant L2-Poincaré inequality. In this article, we study the following properties of harmonic functions, heat kernels and Riesz transforms for p∈(2,∞]: (i) (Gp): Lp-Estimate for the gradient of the associated heat semigroup; (ii) (RHp): Lp-reverse Hölder inequality for the gradients of harmonic functions; (iii) (Rp): Lp-boundedness of the Riesz transform (p

Thayananthan Thayaparan - One of the best experts on this subject based on the ideXlab platform.

  • Profile-Free Launch Point Estimation for Ballistic Targets using Passive Sensors RATNASINGHAM THARMARASA THIAGALINGAM KIRUBARAJAN NANDAKUMARAN NADARAJAH
    2013
    Co-Authors: Yaakov Bar-shalom, Thayananthan Thayaparan
    Abstract:

    This paper considers the estimation of the Launch Points (Lp) of ballistic targets from two or more passive satellite-borne sensors by fusing their angle-only measurements. The targets are assumed to have a two-stage boost phase with a free-flight phase between the two stages. Due to the passive nature of the sensors, there is no measurement during the free-flight motion. It is also assumed that measurements are available only after a few seconds from the launch time due to cloud cover. In the literature, profilebased methods have been proposed to Estimate the target’s launch point and trajectory. Profile-based methods normally result in large errors when there is a mismatch between actual and assumed profiles, which is the case in most scenarios. In this paper, a profile-free method is proposed to Estimate the target states at the End-of-Burnout (EOB) and Lp. Estimates at the EOB are obtained by using forward-filtering with adaptive model selection based on boost phase changes. The Lp Estimates are obtained using smoothing followed by backward prediction. Uncertainties in the motion model and the launch time must be incorporated in the backward prediction. The Lp Estimate and the corresponding error covariance are obtained by incorporating the above uncertainties. Simulation results illustrating the performance of the proposed approach are presented

Coulhon Thierry - One of the best experts on this subject based on the ideXlab platform.

  • Gradient Estimates for heat kernels and harmonic functions
    'Elsevier BV', 2020
    Co-Authors: Coulhon Thierry, Jiang Renjin, Koskela Pekka, Sikora Adam
    Abstract:

    Let (X,d,μ) be a doubling metric measure space endowed with a Dirichlet form E deriving from a “carré du champ”. Assume that (X,d,μ,E) supports a scale-invariant L2-Poincaré inequality. In this article, we study the following properties of harmonic functions, heat kernels and Riesz transforms for p∈(2,∞]: (i) (Gp): Lp-Estimate for the gradient of the associated heat semigroup; (ii) (RHp): Lp-reverse Hölder inequality for the gradients of harmonic functions; (iii) (Rp): Lp-boundedness of the Riesz transform (p