Modern tracking problems require fast, scalable, and robust solutions for tracking multiple targets from noisy sensor data. In this article, an algorithm that has linear computational complexity with respect to the number of targets and measurements is presented. The method is based on the propagation of the first two factorial cumulants of a point process. The algorithm is demonstrated for tracking a million targets in cluttered environments in the fastest time yet for any such solution. A low-computational-complexity solution to the problem of joint multitarget tracking and parameter estimation is also presented. The multitarget filtering approach utilizes a single-cluster point process method for joint multiobject estimation and parameter estimation and is shown to be more computationally efficient and robust than previous implementations.

中文翻译：

---

一种用于大规模多目标跟踪和参数估计的算法

现代跟踪问题需要快速、可扩展且稳健的解决方案，以从嘈杂的传感器数据中跟踪多个目标。在本文中，提出了一种相对于目标数量和测量值具有线性计算复杂度的算法。该方法基于点过程的前两个阶乘累积量的传播。该算法被证明可以在任何此类解决方案的最快时间内跟踪杂乱环境中的一百万个目标。还提出了联合多目标跟踪和参数估计问题的低计算复杂性解决方案。多目标过滤方法利用单簇点处理方法进行联合多目标估计和参数估计，并且比以前的实现方式具有更高的计算效率和鲁棒性。