Drones have become a potent military threat, particularly on the small scale. Nimble multi-rotor drones are fast, difficult to spot, and can cause plenty of harm if allowed to go about their work unhindered. The first step to dealing with this issue is detection—a problem that [Agam Rossen] has put some work into.

The result is VolAnti—an open-source drone detector. This route was chosen as a reliable way to detect incoming multi-rotors, since spinning propellers tend to create a telltale sound that can be plucked from the noise quite specifically. In a world where fiber optic drones eschew RF emissions, it also proves particularly useful for early warning of such craft.

VolAnti relies on a small four-microphone array, with the I2S output of all four mics summed together. The output is then fed into a 2048-point FFT running every 32 ms on an ESP32-S3. A comb score is given to try and pick out different blade rates from 70 Hz to 2000 Hz. Multiple detection algorithms run in parallel, because [Agam] noted a problem—using an adaptive noise floor would miss drones that arrived in the area and hovered in place. With the noise not varying, it would get filtered out by the adaptive floor, so one algorithm in the four runs with no floor to catch drones that aren’t moving. Files are on GitHub for those curious to learn more.

We’ve featured other acoustic detection projects before, too. If you’re working on something similar, or conversely, you have the inside scoop on how to hide a drone’s noise signature, don’t hesitate to let us know on the tipsline.

A fun trick with a pole is to try to balance it so that it can stand on one end. This can be done in a few ways, such as by exerting a force on either end to counterbalance any force that threatens to make it fall over. The approach that [Peter Ryseck] chose was to cobble together what is effectively a flying drone for on top of a standing pole, without cheating such as by simply lifting it off the ground.

Getting to the point where the drone could react quickly enough to changes in the pole’s orientation was the hardest part, as the quieter, larger propellers also have a lot more inertia. This ruled out using 10″ blades, while triple 5″ blades seemed to work well enough. For the avionics a standard quadcopter control board and software is used, with the programming such that it’ll react appropriately without causing additional instabilities.

Naturally making this work took some trial and error, with issues like oscillations plaguing the system. One unexpected problem was that the pole – taken from a pool fishing net – was flexible enough to add its own instabilities to the system. In the video all these issues and their solutions are explained in detail, along with the ultimate result. One very neat solution here for example is to have the pole lean into the wind, which is a more stable configuration than insisting on having the pole be at a perfect ninety degrees with the ground.

When it comes to building real-world flying vehicles, testing can be arduous and expensive. You have to find somewhere to fly, then you have to contend with environmental conditions and the possibility of damaging your craft if things go wrong. Simulation is a great solution to this, allowing testing without so much risk. To that end, [AlexanderRex] whipped up a platform for testing UAVs from the comfort of your computer desk.

PteroSim is intended as a comprehensive test bench for simulating autonomous aerial vehicles. It can run PX4, ArduPilot, and Betaflight binaries right in the simulator. The autopilot code is given simulated sensor data, and in turn responds with actuator commands, just as it would in a real craft. The simulator runs the flight dynamics using JSBSim, and the resulting scene is rendered in Unreal Engine 5.

If you’ve ever wanted to quickly road test different autopilot settings without heading out to the field or risking hardware, this is a great way to do so. It’s hard to beat the speed of iteration that is possible when testing on the computer on your desk. We’ve featured similar work before, too.