Multilateration based ADS-B validation using a Particle Filter

Abstract

Automatic Dependent Surveillance Broadcast (ADS-B) allows aircraft to broadcast their own position, speed, altitude, and other information to ground stations and other nearby aircraft. This information is then used by air traffic control for situational awareness, and collision avoidance. ADS-B spoofing is possible due to the lack of authentication and encryption in the ADS-B protocol. This can result in incorrect decision-making and potential safety hazards. Validation of the location of the ADS-B message is required for Luchtverkeersleiding Nederland (LVNL) such that it can maintain separation minima between civil aircrafts, whilst using ADS-B operationally.
Analysis of possible approaches for ADS-B validation has resulted a multilateration (MLAT) based approach. Time of Arrival (TOA) measurements of the ADS-B messages are used to validate the location. For validation, at least two Ground Stations (GS) are required instead of the four GSs required for a MLAT track, allowing for ADS-B validation in a larger area than it is currently used for in a tracking application. If an ADS-B message is considered validated, its content can be used by ATC. Therefore MLAT based validation results in an increased surveillance coverage. Validation is achieved in two steps, first a tracker is used to compute the state of the target using the TOA measurements, secondly this state is compared to the ADS-B location using a likelihood ratio test.
Tracking is done using a Sequential Importance Resampling (SIR) Particle Filter (PF). Classical PF issues as the degeneracy problem and sample impoverishment problem are mitigated by using a novel sampling method that samples directly from the measurement at the initialization of the SIR filter. Without this novel method a traditional SIR filter (where the proposal density is uniformly distributed) requires roughly a million particles to converge on the location of the target. Below this amount of particles the traditional SIR filter fails. The proposed SIR filter can converge on the location of the target using only 1000 particles.
To provide LVNL options and insights, three different likelihood ratio tests are proposed, namely the Minimum Bayes Risk, The Neyman-Pearson and the Minimax Hypothesis test.
Performance of the algorithm is investigated using a case study where data from LVNL’s Surveillance Data North Sea (SDNS) MLAT system is used. Results have found that each test is capable of correct ADS-B validation. The limiting factor in the validation algorithm is the quality of the state estimate. At lower altitudes (<FL20) state estimation can fail and therefore also the hypothesis test. Above this altitude, spoofed targets can be detected if the distance between the spoofing transmitter and the location inside the spoofed message is roughly 1000 to 2000 meters depending on the hypothesis test used. Horizontally, this falls within LVNL’s separation minima, vertically, this falls outside the separation minima.