Radio Frequency Signal Strength Based Multitarget Tracking With Robust Path Planning.

The proliferation of technologically advanced and mobile devices poses risks to public safety and security. These threats can be mitigated by systems equipped to perform timely identification and tracking of devices and their operators. In pursuit of these capabilities, we propose an automated sensing platform designed specifically for tracking multiple, mobile radio frequency (RF) targets. There are a number of challenges involved with tracking multiple moving RF sources. We formulate the task as an iterative state estimation and path planning process, whereby the sensor platform first estimates the positions of the targets through observation of the RF environment and then plans and executes a movement path. By developing a sensor model informed by RF propagation theory, we construct a particle filter based state estimator with the potential to track multiple targets using only signal strength observations. In addition, we propose a path planning technique rooted in uncertainty minimization and safety based constraints. Finally, we validate the proficiency of the proposed methods with simulated experiments. Through analysis of tracking metrics and localization performance we show the benefits of our proposed active sensing techniques as they apply to tracking multiple RF targets. We demonstrate the robustness of our method to various environmental scenarios by testing with a multitude of realistic and challenging experimental parameters (e.g., speed of the sensor platform, number of targets, speed of targets, level of signal-to-noise ratio (SNR)). The results indicate that our method performs better than other state-of-the-art tracking methods, with significant improvements seen in the most difficult scenarios with higher speed targets. In these and other settings, our method is more often able to localize the targets and with less error and uncertainty in position estimation. We also show that our method is computationally efficient and scales well to an increasing number of targets.