Information driven control can be used to develop intelligent sensors that can optimize their measurement value based on environmental feedback. In object tracking applications, sensor actions are chosen based on the expected reduction in uncertainty also known as information gain. Random finite set (RFS) theory provides a formalism for quantifying and estimating information gain in multi-object tracking problems. However, estimating information gain in these applications remains computationally challenging. This paper presents a new tractable approximation of the RFS expected information gain applicable to sensor control for multi-object search and tracking. Unlike existing RFS approaches, the approximation presented in this paper accounts for noisy measurements, missed detections, false alarms, and object appearance/disappearance. The effectiveness of the information driven sensor control is demonstrated through a multi-vehicle search-while-tracking experiment using real video data from a remote optical sensor.

翻译：由信息驱动的控制可用于开发智能传感器,以便根据环境反馈优化测量价值。在物体跟踪应用中,根据预期的不确定性减少(也称为信息增益)选择传感器行动。随机限量数据集(RFS)理论为量化和估计多物体跟踪问题中的信息增益提供了一种形式主义。然而,估算这些应用中的信息增益仍然具有计算上的挑战性。本文件展示了适用于多物体搜索和跟踪的传感器控制的RFS预期增益的可移植近似新近似值。与现有的RFS方法不同,本文对噪音测量、错失探测、虚假警报和物体外观/消失的近似性作了说明。信息驱动传感器控制的有效性通过使用远程光学传感器的实际视频数据进行多车辆搜索和跟踪实验来证明。