DeepMTL: Deep Learning Based Multiple Transmitter Localization
Caitao Zhan, Mohammad Ghaderibaneh, Pranjal Sahu, Himanshu Gupta
Abstract
In this paper, we address the problem of Multiple Transmitters Localization (MTL), i.e., to determine the locations of potential multiple transmitters in a field, based on readings from a distributed set of sensors. In contrast to the widely studied single transmitter localization problem, the MTL problem has only been studied recently in a few works. MTL problem is of great significance in many applications wherein intruders may be present. E.g., in shared spectrum systems, detection of unauthorized transmitters is imperative to efficient utilization of the shared spectrum.In this paper, we present DeepMTL, a novel deep-learning approach to address the MTL problem. In particular, we frame MTL as a sequence of two steps, each of which is a computer vision problem: image-to-image translation and object detection. The first step of image-to-image translation essentially maps an input image representing sensor readings to an image representing distribution of transmitter locations, and the second object detection step derives precise locations of transmitters from the image of transmitter distributions. For the first step, we design our learning model sen2peak, while for the second step, we customize a state-of-the-art object detection model YOLOv3-cust. We demonstrate the effectiveness of our approach via extensive large-scale simulations, and show that our approach outperforms the previous approaches significantly (by 50% or more) in accuracy performance metrics, and incurs an order of magnitude less latency compared to other prior works.