Unbiased converted measurement manoeuvering target tracking under maximum correntropy criterion

IF 1.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Cognitive Computation and Systems Pub Date : 2020-09-14 DOI:10.1049/ccs.2020.0010
Guoyong Wang, Xiaoliang Feng
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引用次数: 2

Abstract

In this study, the manoeuvering target tracking problem is addressed by using the unbiased converted measurements from a two-dimensional radar system. Due to the fact that radar measurements are usually expressed in polar coordinates while the target motion is described in the Cartesian coordinates, the unbiased converted measurements are utilised to linearise the system model of the manoeuvering target tracking problem in the Cartesian coordinates. The manoeuver acceleration is modelled as the unknown input of the constant velocity kinematic model of the target. First, it is pointed out that the converted measurement noise no longer satisfies Gaussian distribution, even if the raw radar measurement noise is Gaussian noise. In order to solve the manoeuvering target tracking problem with non-Gaussian disturbances, a joint estimation method for the target state and the unknown input is presented under the maximum correntropy criterion. In the simulation, the proposed manoeuvering target tracking method is compared with the one developed on the basis of the traditional Kalman filter. The simulation results verify the effectiveness of the method proposed in this study.

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最大熵准则下的无偏转换测量机动目标跟踪
在本研究中,利用二维雷达系统的无偏转换测量值来解决机动目标跟踪问题。由于雷达测量值通常用极坐标表示,而目标运动用直角坐标描述,因此利用无偏转换后的测量值对机动目标跟踪问题在直角坐标下的系统模型进行线性化。将机动加速度建模为目标等速运动模型的未知输入。首先指出,即使原始雷达测量噪声为高斯噪声,转换后的测量噪声也不再满足高斯分布;为了解决具有非高斯扰动的机动目标跟踪问题,在最大熵准则下,提出了一种目标状态和未知输入的联合估计方法。在仿真中,将所提出的机动目标跟踪方法与基于传统卡尔曼滤波的机动目标跟踪方法进行了比较。仿真结果验证了该方法的有效性。
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来源期刊
Cognitive Computation and Systems
Cognitive Computation and Systems Computer Science-Computer Science Applications
CiteScore
2.50
自引率
0.00%
发文量
39
审稿时长
10 weeks
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