The Experts below are selected from a list of 94539 Experts worldwide ranked by ideXlab platform

Yang Gao - One of the best experts on this subject based on the ideXlab platform.

  • time matching extended Target Probability hypothesis density filter for multi Target tracking of high resolution radar
    Signal Processing, 2019
    Co-Authors: Defu Jiang, Ming Liu, Yiyue Gao, Yang Gao
    Abstract:

    Abstract Extended Target Probability hypothesis density (ET-PHD) filters have recently become popular owing to their relatively simple recursion processes, which makes them suitable for use in applications requiring real-time results, such as radar multi-Target tracking. However, in the classic ET-PHD filters, the measurements of different Targets are generally considered to be generated at the end of each scan. With this assumption, the measurement time diversity in radar applications cannot be modeled. To address this shortcoming, a novel time-matching ET-PHD filter in which a multi-prediction filtering framework is applied, and the true measurement times are used in the PHD propagation of each cell, i.e., prediction and correction, is proposed in this paper. In addition, a pre-partitioning strategy is employed to reduce the computational complexity of the proposed filter. The results of simulations conducted using the gamma Gaussian inverse Wishart PHD filter indicate that the proposed pre-partitioning-based time-matching ET-PHD filter is superior to standard filters in terms of both estimation accuracy and real-time performance.

Yan Liang - One of the best experts on this subject based on the ideXlab platform.

  • adaptive collaborative gaussian mixture Probability hypothesis density filter for multi Target tracking
    Sensors, 2016
    Co-Authors: Feng Yang, Yongqi Wang, Hao Chen, Pengyan Zhang, Yan Liang
    Abstract:

    In this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-Target tracking with automatic track extraction. Based on the evolutionary difference between the persistent Targets and the birth Targets, the measurements are adaptively partitioned into two parts, persistent and birth measurement sets, for updating the persistent and birth Target Probability Hypothesis Density, respectively. Furthermore, the collaboration mechanism of multiple Probability hypothesis density (PHDs) is established, where tracks can be automatically extracted. Simulation results reveal that the proposed filter yields considerable computational savings in processing requirements and significant improvement in tracking accuracy.

Chongzhao Han - One of the best experts on this subject based on the ideXlab platform.

  • two measurement set partitioning algorithms for the extended Target Probability hypothesis density filter
    Sensors, 2019
    Co-Authors: Yulan Han, Chongzhao Han
    Abstract:

    The extended Target Probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from Target to Target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between measurements. To tackle the problem, two measurement set partitioning approaches, the shared nearest neighbors similarity partitioning (SNNSP) and SNN density partitioning (SNNDP), are proposed in this paper. In SNNSP, the shared nearest neighbors (SNN) similarity, which incorporates the neighboring measurement information, is introduced to DP instead of the Mahalanobis distance between measurements. Furthermore, the SNNDP is developed by combining the DBSCAN algorithm with the SNN similarity together to enhance the reliability of partitions. Simulation results show that the ET-PHD filters based on the two proposed partitioning algorithms can achieve better tracking performance with less computation than the compared algorithms.

Raphael T. Haftka - One of the best experts on this subject based on the ideXlab platform.

  • Application of bootstrap method in conservative estimation of reliability with limited samples
    Structural and Multidisciplinary Optimization, 2009
    Co-Authors: Victor Picheny, Nam-ho Kim, Raphael T. Haftka
    Abstract:

    Accurate estimation of reliability of a system is a challenging task when only limited samples are available. This paper presents the use of the bootstrap method to safely estimate the reliability with the objective of obtaining a conservative but not overly conservative estimate.The performance of the bootstrap method is compared with alternative conservative estimation methods, based on biasing the distribution of system response. The relationship between accuracy and conservativeness of the estimates is explored for normal and lognormal distributions. In particular, detailed results are presented for the case when the goal has a 95% likelihood to be conservative. The bootstrap approach is found to be more accurate for this level of conservativeness. We explore the influence of sample size and Target Probability of failure on the quality of estimates, and show that for a given level of conservativeness, small sample sizes and low probabilities of failure can lead to a high likelihood of large overestimation. However, this likelihood can be reduced by increasing the sample size. Finally, the conservative approach is applied to the reliability-based optimization of a composite panel under thermal loading.

  • reliability based design optimization using probabilistic sufficiency factor
    Structural and Multidisciplinary Optimization, 2004
    Co-Authors: X Qu, Raphael T. Haftka
    Abstract:

    A probabilistic sufficiency factor approach is proposed that combines safety factor and Probability of failure. The probabilistic sufficiency factor approach represents a factor of safety relative to a Target Probability of failure. It provides a measure of safety that can be used more readily than the Probability of failure or the safety index by designers to estimate the required weight increase to reach a Target safety level. The probabilistic sufficiency factor can be calculated from the results of Monte Carlo simulation with little extra computation. The paper presents the use of probabilistic sufficiency factor with a design response surface approximation, which fits it as a function of design variables. It is shown that the design response surface approximation for the probabilistic sufficiency factor is more accurate than that for the Probability of failure or for the safety index. Unlike the Probability of failure or the safety index, the probabilistic sufficiency factor does not suffer from accuracy problems in regions of low Probability of failure when calculated by Monte Carlo simulation. The use of the probabilistic sufficiency factor accelerates the convergence of reliability-based design optimization.

Jeanmichel Loubes - One of the best experts on this subject based on the ideXlab platform.

  • central limit theorem for empirical transportation cost in general dimension
    Annals of Probability, 2019
    Co-Authors: Eustasio Del Barrio, Jeanmichel Loubes
    Abstract:

    We consider the problem of optimal transportation with quadratic cost between a empirical measure and a general Target Probability on R d , with d ≥ 1. We provide new results on the uniqueness and stability of the associated optimal transportation potentials , namely, the minimizers in the dual formulation of the optimal transportation problem. As a consequence, we show that a CLT holds for the empirical transportation cost under mild moment and smoothness requirements. The limiting distributions are Gaussian and admit a simple description in terms of the optimal transportation potentials.

  • central limit theorem for empirical transportation cost in general dimension
    arXiv: Probability, 2017
    Co-Authors: Eustasio Del Barrio, Jeanmichel Loubes
    Abstract:

    We consider the problem of optimal transportation with quadratic cost between a empirical measure and a general Target Probability on R d , with d $\ge$ 1. We provide new results on the uniqueness and stability of the associated optimal transportation potentials , namely, the minimizers in the dual formulation of the optimal transportation problem. As a consequence, we show that a CLT holds for the empirical transportation cost under mild moment and smoothness requirements. The limiting distributions are Gaussian and admit a simple description in terms of the optimal transportation potentials.