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arXiv 2012.02340cs.ROcs.MA
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基于PHD滤波器的多四旋翼分散多目标跟踪

Decentralized Multi-target Tracking with Multiple Quadrotors using a PHD Filter

Arizona State University（亚利桑那州立大学）

Aniket Shirsat, Spring Berman

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15 pages
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摘要：

我们考虑的情况下，一组四旋翼的任务是在一个未知的，有界的环境中跟踪多个固定的目标。四旋翼沿着覆盖在环境上的空间网格搜索目标，同时在由离散时间离散状态（DTDS）马尔可夫链建模的该网格上执行随机行走。四旋翼可以将它们对目标位置的估计传输到占据它们在网格上的当前位置的其他四旋翼;因此，它们的通信网络是时变的并且不一定连接。我们模型的搜索过程作为一个更新奖励过程的基础DTDS马尔可夫链。为了适应每个四旋翼在探索环境时观察到的目标集的变化，以及四旋翼对目标的测量的不确定性，我们用随机有限集（RFS）来表示跟踪问题。四旋翼使用基于射频的概率假设密度（PHD）滤波器来估计目标的数量及其位置。我们提出了一个理论估计框架，基于高斯混合配方的PHD滤波器，和初步的模拟结果对扩展现有的方法，基于RFS的多目标跟踪到一个分散的多机器人多目标跟踪策略。我们验证了这种方法与不同密度的机器人和目标的多目标跟踪场景的模拟，我们评估的平均时间所需的机器人在每个场景中达成协议的一组共同的目标。

英文摘要：

We consider a scenario in which a group of quadrotors is tasked at tracking multiple stationary targets in an unknown, bounded environment. The quadrotors search for targets along a spatial grid overlaid on the environment while performing a random walk on this grid modeled by a discrete-time discrete-state (DTDS) Markov chain. The quadrotors can transmit their estimates of the target locations to other quadrotors that occupy their current location on the grid; thus, their communication network is time-varying and not necessarily connected. We model the search procedure as a renewal-reward process on the underlying DTDS Markov chain. To accommodate changes in the set of targets observed by each quadrotor as it explores the environment, along with uncertainties in the quadrotors' measurements of the targets, we formulate the tracking problem in terms of Random Finite Sets (RFS). The quadrotors use RFS-based Probability Hypothesis Density (PHD) filters to estimate the number of targets and their locations. We present a theoretical estimation framework, based on the Gaussian Mixture formulation of the PHD filter, and preliminary simulation results toward extending existing approaches for RFS-based multi-target tracking to a decentralized multi-robot strategy for multi-target tracking. We validate this approach with simulations of multi-target tracking scenarios with different densities of robots and targets, and we evaluate the average time required for the robots in each scenario to reach agreement on a common set of targets.

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