Thomas Nussbaumer’s research while affiliated with RUAG Technology and other places

There is a great interest in the generation of plausible drone signals in various applications, e.g. for auralization purposes or the compilation of training data for detection algorithms. Here, a methodology is presented which synthesises realistic immission signals based on laboratory recordings and subsequent signal processing. The transformation of a lab drone signal into a virtual field microphone signal has to consider a constant pitch shift to adjust for the manoeuvre specific rotational speed and the corresponding frequency dependent emission strength correction, a random pitch shift variation to account for turbulence induced rotational speed variations in the field, Doppler frequency shift and time and frequency dependent amplitude adjustments according to the different propagation effects. By evaluation of lab and field measurements, the relevant synthesizer parameters were determined. It was found that for the investigated set of drone types, the vertical radiation characteristics can be successfully described by a generic frequency dependent directivity pattern. The proposed method is applied to different drone models with a total weight between 800 g and 3.4 kg and is discussed with respect to its abilities and limitations comparing both, recordings taken in the lab and the field.

Based on the steadily growing use of mini-UAVs for numerous civilian and military applications, mini-UAVs have been recognized as an increasing potential threat. Therefore, counter-UAV solutions addressing the peculiarities of this class of UAVs have recently received a significant amount of attention. Reliable detection, localization, identification and tracking represents a fundamental prerequisite for such counter-UAV systems. In this paper, we focus on the assessment of different sensor technologies and their ability to detect mini-UAVs in a representative rural Swiss environment. We conducted a field trial in August 2015, using different, primarily short range, experimental sensor systems from armasuisse and selected research partners. After an introduction into the challenges for UAV detection in regions with strong topographic relief, we will introduce the experimental setup and describe the key results from this joint experiment.

Recent events of drones flying over city centers, official buildings and nuclear installations stressed the growing threat of uncontrolled drone proliferation and the lack of real countermeasure. Indeed, detecting and tracking them can be difficult with traditional techniques. A system to acoustically detect and track small moving objects, such as drones or ground robots, using acoustic cameras is presented. The described sensor, is completely passive, and composed of a 120-element microphone array and a video camera. The acoustic imaging algorithm determines in real-time the sound power level coming from all directions, using the phase of the sound signals. A tracking algorithm is then able to follow the sound sources. Additionally, a beamforming algorithm selectively extracts the sound coming from each tracked sound source. This extracted sound signal can be used to identify sound signatures and determine the type of object. The described techniques can detect and track any object that produces noise (engines, propellers, tires, etc). It is a good complementary approach to more traditional techniques such as (i) optical and infrared cameras, for which the object may only represent few pixels and may be hidden by the blooming of a bright background, and (ii) radar or other echo-localization techniques, suffering from the weakness of the echo signal coming back to the sensor. The distance of detection depends on the type (frequency range) and volume of the noise emitted by the object, and on the background noise of the environment. Detection range and resilience to background noise were tested in both, laboratory environments and outdoor conditions. It was determined that drones can be tracked up to 160 to 250 meters, depending on their type. Speech extraction was also experimentally investigated: the speech signal of a person being 80 to 100 meters away can be captured with acceptable speech intelligibility.