# RSS-Based Detection of Drones in the Presence of RF Interferers

## TL;DR

This paper presents an RSS-based drone detection method that utilizes existing wireless infrastructure to identify drones amidst RF interference and noise, providing analytical performance metrics and optimizing sensor deployment.

## Contribution

It introduces an analytical framework for drone detection using RSS in interference-rich environments and determines optimal sensor density for maximum detection probability.

## Key findings

- Analytical expressions for detection and false alarm probabilities.
- Impact of interference and propagation on detection performance.
- Optimal sensor density for maximizing detection coverage.

## Abstract

Drones will have extensive use cases across various commercial, government, and military sectors, ranging from delivery of consumer goods to search and rescue operations. To maintain the safety and security of people and infrastructure, it becomes critically important to quickly and accurately detect non-cooperating drones. In this paper we formulate a received signal strength (RSS) based detector, leveraging the existing wireless infrastructures that might already be serving other devices. Thus the detector can detect the presence of a drone signal buried in radio frequency (RF) interference and thermal noise, in a mixed line-of-sight (LOS) and non-LOS (NLOS) environment. We develop analytical expressions for the probability of false alarm and the probability of detection of a drone, which quantify the impact of aggregate interference and air-to-ground (A2G) propagation characteristics on the detection performance of individual sensors. We also provide analytical expressions for the average network probability of detection, which capture the impact of sensor density on a network's detection coverage. Finally, we find the critical sensor density that maximizes the average network probability of detection for a given requirement of the probability of false alarm.