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Extended object tracking using random matrix with converted measurements
IET Radar Sonar and Navigation ( IF 1.7 ) Pub Date : 2020-06-25 , DOI: 10.1049/iet-rsn.2019.0512
Zhifei Li 1 , Jianyun Zhang 1 , Jiegui Wang 1 , Qingsong Zhou 1
Affiliation  

The random matrix approach for extended object tracking (EOT) is appealing. This approach assumes that the measurements are linear in the kinematic state and measurement noise. In many practical applications, however, this linear condition cannot always be satisfied. First, this study derives a linearised measurement model. The model first employs a decorrelated unbiased technique to convert the non-linear measurements into Cartesian coordinates. Then, due to the property of the random matrix as the covariance of the extension noise, the model reformulates the likelihood function by calculating a product of two multivariate Gaussian distributions. The proposed linearised measurement model can be incorporated into existing random matrix approaches. Secondly, to describe a more complicated dynamics without the restriction of the existing random matrix framework, the authors propose a variational Bayesian (VB) approach for EOT. The VB approach minimises the Kullback–Leibler divergence between the true and approximate posterior density to obtain a convergent solution. The effectiveness of the proposed linearised model and the VB approach is illustrated by simulation results.

中文翻译:

使用随机矩阵和转换后的测量值进行扩展的对象跟踪

用于扩展对象跟踪(EOT)的随机矩阵方法很有吸引力。该方法假定在运动状态和测量噪声下测量值是线性的。然而,在许多实际应用中,这种线性条件不能总是得到满足。首先,这项研究得出了线性化的测量模型。该模型首先采用去相关无偏技术将非线性测量结果转换为笛卡尔坐标。然后,由于随机矩阵作为扩展噪声的协方差的特性,该模型通过计算两个多元高斯分布的乘积来重新构造似然函数。可以将提出的线性化测量模型并入现有的随机矩阵方法中。其次,为了描述更复杂的动力学而不限制现有随机矩阵框架,作者提出了变分贝叶斯(VB)方法进行EOT。VB方法使真实和近似后验密度之间的Kullback-Leibler差异最小化,以获得收敛解。仿真结果说明了所提出的线性化模型和VB方法的有效性。
更新日期:2020-06-26
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