In this paper, we propose a computationally efficient sensor selection approach for maneuvering target tracking using a sensor network with communication bandwidth constraints, given limited prior information on the target maneuvering models. We formulate the stochastic sensor selection problem as a linear programming problem which consists of two easily implementable steps. First, the Cramér–Rao lower bound corresponding to the sensor subset is derived as the objective function of the proposed sensor selection method based on a partially observable Markov decision process. Second, the target trajectory is modeled by a function of time to enable online target tracking which is free of the conventional, a priori Markov modeling of the target dynamics. We demonstrate the effectiveness of our method through several numerical examples.

在本文中，我们提出了一种计算有效的传感器选择方法，用于使用具有通信带宽限制的传感器网络进行机动目标跟踪，前提是目标机动模型的先验信息有限。我们将随机传感器选择问题表述为一个线性规划问题，该问题由两个易于实现的步骤组成。首先，对应于传感器子集的 Cramér-Rao 下界被导出为基于部分可观察马尔可夫决策过程的传感器选择方法的目标函数。其次，目标轨迹是通过时间函数建模的，以实现在线目标跟踪，而无需对目标动力学进行传统的先验马尔可夫建模。我们通过几个数值例子证明了我们方法的有效性。