  In this work, we argue that current state-of-the-art methods of aircraft
localization such as multilateration are insufficient, in particular for modern
crowdsourced air traffic networks with random, unplanned deployment geometry.
We propose an alternative, a grid-based localization approach using the
k-Nearest Neighbor algorithm, to deal with the identified shortcomings. Our
proposal does not require any changes to the existing air traffic protocols and
transmitters, and is easily implemented using only low-cost,
commercial-off-the-shelf hardware.
  Using an algebraic multilateration algorithm for comparison, we evaluate our
approach using real-world flight data collected with our collaborative sensor
network OpenSky. We quantify its effectiveness in terms of aircraft location
accuracy, surveillance coverage, and the verification of false position data.
Our results show that the grid-based approach can increase the effective air
traffic surveillance coverage compared to multilateration by a factor of up to
2.5. As it does not suffer from dilution of precision, it is much more robust
in noisy environments and performs better in pre-existing, unplanned receiver
deployments.
  We further find that the mean aircraft location accuracy can be increased by
up to 41% in comparison with multilateration while also being able to pinpoint
the origin of potential spoofing attacks conducted from the ground.
