Sensor Assignment Algorithms to Improve Observability While Tracking Targets
Sensor Assignment Algorithms to Improve Observability While Tracking Targets
复制标题
传感器分配算法可提高跟踪目标时的可观测性
DOI：
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复制
发表时间：
2017
影响因子：
7.8
通讯作者：
Pratap Tokekar
中科院分区：
文献类型：
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作者：
Lifeng Zhou;Pratap Tokekar
在本文中，我们研究了多目标跟踪的两个传感器分配问题，目的是提高底层估计器的可观测性。我们将可观测性矩阵的各种度量视为分配值函数。我们首先研究通用版本，其中传感器必须组成团队来跟踪单个目标。如果价值函数是单调递增和子模的，那么贪心算法会产生 $1/2$ 近似值。然后我们研究一个受限版本，其中必须为每个目标分配恰好两个传感器。我们针对这个问题提出了一个 $1/3$ 近似算法，它适用于任意值函数（不一定是子模或单调）。除了近似算法之外，我们还提出了可观测性度量的各种属性。我们证明可观测性矩阵的条件数的逆既不是单调的也不是子模的，但提出了其他措施。具体来说，我们证明对称可观测性矩阵的迹和秩是单调和次模的，并且当矩阵是非奇异时，对称可观测性矩阵的对数行列式是单调和次模的。如果目标的运动模型未知，则无法准确计算逆函数。相反，我们提出了距离传感器的下限。除了理论结果之外，我们还通过模拟来实证评估我们的结果。
In this paper, we study two sensor assignment problems for multitarget tracking with the goal of improving the observability of the underlying estimator. We consider various measures of the observability matrix as the assignment value function. We first study the general version where the sensors must form teams to track individual targets. If the value function is monotonically increasing and submodular, then a greedy algorithm yields a $1/2$–approximation. We then study a restricted version where exactly two sensors must be assigned to each target. We present a $1/3$–approximation algorithm for this problem, which holds for arbitrary value functions (not necessarily submodular or monotone). In addition to approximation algorithms, we also present various properties of observability measures. We show that the inverse of the condition number of the observability matrix is neither monotone nor submodular, but present other measures that are. Specifically, we show that the trace and rank of the symmetric observability matrix are monotone and submodular and the log determinant of the symmetric observability matrix is monotone and submodular when the matrix is nonsingular. If the target's motion model is not known, the inverse cannot be computed exactly. Instead, we present a lower bound for distance sensors. In addition to theoretical results, we evaluate our results empirically through simulations.
DOI：
10.1109/icra.2017.7989244
发表时间：
2017-05
期刊：
2017 IEEE International Conference on Robotics and Automation (ICRA)
影响因子：
--
作者：
Lifeng Zhou;Pratap Tokekar
通讯作者：
Lifeng Zhou;Pratap Tokekar