Distinguishing Drones from Birds in a UAV Searching Laser Scanner Based on Echo Depolarization Measurement

Abstract

Widespread availability of drones is associated with many new fascinating possibilities, which were reserved in the past for few. Unfortunately, this technology also has many negative consequences related to illegal activities (surveillance, smuggling). For this reason, particularly sensitive areas should be equipped with sensors capable of detecting the presence of even miniature drones from as far away as possible. A few techniques currently exist in this field; however, all have significant drawbacks. This study addresses a novel approach for small (<5 kg) drones detection technique based on a laser scanning and a method to discriminate UAVs from birds. The latter challenge is fundamental in minimizing the false alarm rate in each drone monitoring equipment. The paper describes the developed sensor and its performance in terms of drone vs. bird discrimination. The idea is based on simple cross-polarization ratio analysis of the optical echo received as a result of laser backscattering on the detected object. The obtained experimental results show that the proposed method does not always guarantee 100 percent discrimination efficiency, but provides certain confidence level distribution. Nevertheless, due to the hardware simplicity, this approach seems to be a valuable addition to the developed anti-drone laser scanner.

Keywords: UAV detection; anti-drone system; drone detection; drone monitoring; drone vs. bird discrimination; laser scanner.

Figure 1

Heuristic presentation of a reflection of light from rough surface, as a combination of several physical mechanisms. Visualization of magnified rough surface micro-relief and six possible examples of how a ray of light can interact with it.

Figure 2

Laboratory setup for experimental search of optimum output/measured polarizations combination in terms of feathers vs. artificial surfaces discrimination.

Figure 3

Optical configuration of the developed scanner (rotating mirror omitted).

Figure 4

Photography of the constructed anti-drone laser scanner prototype.

Figure 5

Samples representing birds.

Figure 6

Samples representing drones.

Figure 7

Photo of the experimental test site.

Figure 8

Results of cross-polarization ratio field measurements using the developed laser scanner module. Samples naming corresponds to Figure 5 and Figure 6 (red color—drones; green color—birds).

Figure 9

(Left) histograms of separated data sets corresponding to drones (in red) and birds (in green). (Right) normal distributions (normalized to unity) fitted to both histograms.

Figure 10

Probability density functions of δ (drones curve—in red, birds curve—in green).

Figure 11

Drones vs. birds classification probability factor.

Figure 12

Comparison of the measured δ obtained for various distances (right) with two Gaussian curves corresponding to drones (red) and feathers (green) obtained in the static measurements (left). Db, Ds, Df – symbols associated with the specific drones models used in the experiments.