观测误差
传感器融合
融合
高斯噪声
熵(时间箭头)
噪音(视频)
计算机科学
噪声测量
高斯分布
人工智能
算法
数学
声学
统计物理学
物理
降噪
统计
量子力学
语言学
图像(数学)
哲学
作者
Minzhe Li,Zhongliang Jing,Hongyu Zhu,Yiren Song
标识
DOI:10.1016/j.dsp.2021.103377
摘要
In this paper, a novel multi-sensor measurement fusion method is proposed based on minimum error entropy criterion for nonlinear state estimation in non-Gaussian measurement noise. Through cubature-quadrature transform, a mixture error entropy cost function is defined to capture the high order statistics of multi-source measurement error. Based on the centralized measurement model, a measurement fusion information filter is developed by minimizing the mixture error entropy cost. The fixed point iteration approach is employed to calculate the estimates, and the square root implementation of the proposed filter is also provided. Further, the convergence conditions of the proposed information filter are derived. Simulations are performed with different dimensional measurements to demonstrate the effectiveness of the proposed algorithm. It is shown that, the estimation performance of the proposed measurement fusion information filter is better than that of traditional Kalman and Huber's filter based fusion methods against outliers and Gaussian mixture noises.
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