Review of Intentional Electromagnetic Interference on UAV Sensor Modules and Experimental Study

Abstract

With the advancement of technology, Unmanned Aerial Vehicles (UAVs), also known as drones, are being used in numerous applications. However, the illegal use of UAVs, such as in terrorism and spycams, has also increased, which has led to active research on anti-drone methods. Various anti-drone methods have been proposed over time; however, the most representative method is to apply intentional electromagnetic interference to drones, especially to their sensor modules. In this paper, we review various studies on the effect of intentional electromagnetic interference (IEMI) on the sensor modules. Various studies on IEMI sources are reviewed and classified on the basis of the power level, information needed, and frequency. To demonstrate the application of drone-sensor modules, major sensor modules used in drones are briefly introduced, and the setup and results of the IEMI experiment performed on them are described. Finally, we discuss the effectiveness and limitations of the proposed methods and present perspectives for further research necessary for the actual application of anti-drone technology.

Keywords: UAV; anti-drone; drone; intentional electromagnetic interference; sensor module.

Figure 1

Block diagram of various modules in drone and the signal flow.

Figure 2

Real time sensor data monitoring using ‘QGroundControl’ software.

Figure 3

Block diagram for sensor data monitoring using log files.

Figure 4

Diagram and experimental setup of near-field scanner.

Figure 5

Result of flight controller near-field scanning. Both the front and back sides were scanned, and the probe tip is effective from 30 MHz to 3 GHz. (a) Near-field scanning results showing peak value of each frequency. (b) 2D electric-field distribution at the frequency with the highest peak value for each aspect. (Front: 143.2 MHz, Back: 256 MHz).

Figure 5

Result of flight controller near-field scanning. Both the front and back sides were scanned, and the probe tip is effective from 30 MHz to 3 GHz. (a) Near-field scanning results showing peak value of each frequency. (b) 2D electric-field distribution at the frequency with the highest peak value for each aspect. (Front: 143.2 MHz, Back: 256 MHz).

Figure 6

Experimental setup for high power IEMI experiments. (a) Experimental setup diagram; (b) Realized experimental setup.

Figure 6

Experimental setup for high power IEMI experiments. (a) Experimental setup diagram; (b) Realized experimental setup.

Figure 7

Monitored gyroscope data with high power IEMI attack.

Figure 8

Experimental setup for low-power IEMI experiment on custom drone (Pixhawk 4). (a) Experimental setup diagram; (b) Realized experimental setup.

Figure 8

Experimental setup for low-power IEMI experiment on custom drone (Pixhawk 4). (a) Experimental setup diagram; (b) Realized experimental setup.

Figure 9

Examples of received videos in each status.

Figure 10

Experimental setup for low power IEMI experiment on commercial drone (DJI Mavic Air2). (a) Experimental setup diagram (Top view); (b) Experimental setup diagram (Side view); (c) Realized experimental setup (Front); (d) Realized experimental setup (Side).

Figure 11

Results of low-power IEMI on camera module using digital communication scheme. The video status changed for Scenario 1 and Scenario 2. (a) Lagging occurred when the attack signal level exceeded 6 dBm. Distortions were not observed. (b) Distortion occurred when the attack signal overlapped with the video transmission channel. When the attack signal exceeded the channel frequency, the video quality was recovered.

Figure 12

Experimental setup for non-RF IEMI on optical flow. (a) Laser attack off. (b) Laser attack on. (c) Filmed image before the laser attack was applied. To easily indicate changes, an object with written letters was placed in front of the camera. (d) Filmed image when the laser attack was applied. A distorted image was received.

Figure 13

Monitored optical-flow sensor data. Abnormally calculated value led to the corresponding compensation value that might be critical for a stable flight. (a) When the attack was applied, the sensor calculated abnormal gyro data and the corresponding compensation value. (b) When the attack was applied, the sensor calculated abnormal pixel flow and the corresponding compensation value.