Adaptive Radio Frequency Target Localization

Mobile radio frequency (RF) target localization is a widely studied field with a wide variety of applications including health monitoring of electronic devices and search and rescue. This recently has been done using a greedy approach, where a sensor is moved closer to the target after each measurement. However, this does not allow for other constraints, such as maintaining a fixed distance from the target or optimization of energy consumption. Studies in the field have used techniques such as machine learning to localize static targets with received RF signals, and dynamic targets with received RF signals combined with line-of-sight observations. A recent study simulated localization of static and dynamic targets through RF signal characterization without the need for line-of-sight observation while also maintaining the previously mentioned constraints. This was done by modeling the problem as a Partially Observable Markov Decision Process (POMDP) and was solved through the use of particle filtering and reinforcement learning. The purpose of this work is to build upon this prior study by training a deep neural network in a simulated environment and applying inference in the real world. This led to various changes to the model that better matched real-world scenarios. By defining the metric of success as the distance between the actual location of the device and the estimated location of the device, it was shown that the model was able to accurately locate static devices within the measured standard deviation of the signal strength. Future work includes the use of autonomous units such as drones, as well as extending the capabilities of the model to localize real-world dynamic targets.