Tracking for multiple extended objects with a complex extension is a challenging radar technology; especially for small back-scattering objects such as extended stealth targets (ESTs). This work provides a new approach for ESTs tracking under the non-linear dynamic system based on track-before-detect (TBD) approach. The sequential Monte Carlo multi-Bernoulli (SMC-MB) filter provides a good framework to cope with TBD approach. Recently, the SMC-MB filter with a random matrix model (RMM) has been applied for tracking extended targets by additional state variables. However, SMC-MB-RMM filter is implemented with known detection probability, which is unsuitable for ESTs-TBD scenario. Therefore, we introduce a new SMC-MB-RMM filter hybrid with TBD algorithm, which is effective method to track ESTs. In ESTs-RMM-TBD scenarios, although the extension ellipsoid is effective, it may not be accurate enough due to lacking the useful parameters, such as shape, size and orientation. Therefore, we propose a ESTs-Sub-RMM-TBD composed of sub-ellipses; each one is applied by RMM. Based on such models, a SMC-Sub-RMM-MB-TBD algorithm is applied to estimate extensions and kinematic states for each sub-objects. The simulation results show that the presented filter has a small OSPA errors and more accurate cardinality calculation than the other algorithms.

跟踪具有复杂扩展的多个扩展对象是一项具有挑战性的雷达技术。特别是对于小型向后散射物体，例如扩展的隐形目标（EST）。这项工作为基于检测前跟踪（TBD）方法的非线性动态系统下的EST跟踪提供了一种新方法。顺序蒙特卡罗多伯努利（SMC-MB）滤波器提供了一个很好的框架来应对TBD方法。最近，具有随机矩阵模型（RMM）的SMC-MB滤波器已被用于通过附加状态变量跟踪扩展目标。但是，SMC-MB-RMM过滤器以已知的检测概率实施，这不适用于ESTs-TBD方案。因此，我们引入了一种新的结合了TBD算法的SMC-MB-RMM滤波器，它是跟踪EST的有效方法。在ESTs-RMM-TBD方案中，尽管扩展椭球有效，但由于缺少有用的参数（例如形状，大小和方向），它可能不够准确。因此，我们提出了由子椭圆组成的ESTs-Sub-RMM-TBD。每个都由RMM应用。基于这样的模型，SMC-Sub-RMM-MB-TBD算法被应用于估计每个子对象的扩展和运动状态。仿真结果表明，与其他算法相比，该滤波器具有较小的OSPA误差和更精确的基数计算。SMC-Sub-RMM-MB-TBD算法应用于估计每个子对象的扩展和运动状态。仿真结果表明，与其他算法相比，该滤波器具有较小的OSPA误差和更精确的基数计算。SMC-Sub-RMM-MB-TBD算法应用于估计每个子对象的扩展和运动状态。仿真结果表明，与其他算法相比，该滤波器具有较小的OSPA误差和更精确的基数计算。