Abstract In this paper, the problem of joint detection, tracking and classification (JDTC) of multiple extended objects (EOs) is addressed using the random finite set (RFS) theory. In the proposed method, the shape of an EO is restricted to an ellipse that can be easily modelled by a random matrix. The classification of an EO is estimated on the basis of size information with respect to its extended part. Furthermore, the extended object RFS is proposed to be modelled as a multi-Bernoulli (MeMBer) process, then the MeMBer filter is naturally adopted to propagate the EO posteriors. In this paper, in order to achieve the closed-form solution, we model the spatial probability density function (SPDF) of each Bernoulli component as the mixture Gaussian inverse Wishart (GIW) process for the JDTC problem. Compared to the existing JDTC strategies, the proposed algorithm, named as JDTC-GIW-MeMBer filter, is demonstrated via simulations to achieve better performance to the existing algorithms on detection and tracking of the EO centers, the estimation accuracy of the extended states, and EO classifications.

中文翻译：

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基于JDTC-GIW-MeMBer滤波器的多扩展目标联合检测、跟踪和分类

摘要 在本文中，使用随机有限集 (RFS) 理论解决了多个扩展对象 (EO) 的联合检测、跟踪和分类 (JDTC) 问题。在所提出的方法中，EO 的形状被限制为一个椭圆，该椭圆可以很容易地由随机矩阵建模。EO 的分类是根据其扩展部分的大小信息来估计的。此外，建议将扩展对象 RFS 建模为多伯努利 (MeMBer) 过程，然后自然采用 MeMBer 滤波器来传播 EO 后验。在本文中，为了实现封闭形式的解决方案，我们将每个伯努利分量的空间概率密度函数 (SPDF) 建模为 JDTC 问题的混合高斯逆 Wishart (GIW) 过程。与现有的 JDTC 策略相比，