This paper considers the problem of tracking a large-scale number of group
targets. Usually, multi-target in most tracking scenarios are assumed to have
independent motion and are well-separated. However, for group target tracking
(GTT), the targets within groups are closely spaced and move in a coordinated
manner, the groups can split or merge, and the numbers of targets in groups may
be large, which lead to more challenging data association, filtering and
computation problems. Within the belief propagation (BP) framework, we propose
a scalable group target belief propagation (GTBP) method by jointly inferring
target existence variables, group structure, data association and target
states. The method can efficiently calculate the approximations of the marginal
posterior distributions of these variables by performing belief propagation on
the devised factor graph. As a consequence, GTBP is capable of capturing the
changes in group structure, e.g., group splitting and merging. Furthermore, we
model the evolution of targets as the co-action of the group or single-target
motions specified by the possible group structures and corresponding
probabilities. This flexible modeling enables seamless and simultaneous
tracking of multiple group targets and ungrouped targets. Particularly, GTBP
has excellent scalability and low computational complexity. It not only
maintains the same scalability as BP, i.e., scaling linearly in the number of
sensor measurements and quadratically in the number of targets, but also only
scales linearly in the number of preserved group partitions. Finally, numerical
experiments are presented to demonstrate the effectiveness and scalability of
the proposed GTBP method.

该论文研究了大规模组目标跟踪问题，提出了一种基于置信传播（BP）框架的可扩展组目标置信传播（GTBP）方法，将目标存在变量、组结构、数据关联和目标状态联合推断计算，具有良好的可扩展性和低计算复杂度。

使用信念传播对群体目标和非群组目标实现无缝跟踪

Seamless Tracking of Group Targets and Ungrouped Targets Using Belief Propagation

We consider partially observable Markov decision processes (POMDPs) with a
set of target states and every transition is associated with an integer cost.
The optimization objective we study asks to minimize the expected total cost
till the target set is reached, while ensuring that the target set is reached
almost-surely (with probability 1). We show that for integer costs
approximating the optimal cost is undecidable. For positive costs, our results
are as follows: (i) we establish matching lower and upper bounds for the
optimal cost and the bound is double exponential; (ii) we show that the problem
of approximating the optimal cost is decidable and present approximation
algorithms developing on the existing algorithms for POMDPs with finite-horizon
objectives. While the worst-case running time of our algorithm is double
exponential, we also present efficient stopping criteria for the algorithm and
show experimentally that it performs well in many examples of interest.

本文研究部分可观察马尔可夫决策过程 (POMDPs)，带有一组目标状态并且每个转移都有一个整数成本。研究的最优化目标是在确保（概率为 1）几乎达到目标集时最小化预期总成本。我们证明，对于整数成本，近似最优成本是不可判定的。对于正成本，我们的结果有：（i）我们建立了最优成本的匹配下限和上限，上限是双指数；（ii）我们表明，近似最优成本的问题是可判定的，并且提出了建立在具有有限时间段目标的 POMDP 算法上的近似算法。虽然这个算法的最坏运行时间是双指数的，但我们还介绍了算法的有效停止标准，并实验性地表明它在许多有意义的示例中表现良好。