Distributed Cognitive Positioning System Based on Nearest Neighbor Association and Multi-Point Filter Initiation for UAVs Using DTMB and INS

Location is critical for the safe and effective completion of Unmanned Aerial Vehicle (UAV) missions. Since positioning errors tend to accumulate over time, uncorrected measurements from Inertial Navigation Systems (INSs) are unreliable. Aiming for UAV self-positioning under the challenges of a Global Navigation Satellite System (GNSS), this article integrates Digital Terrestrial Multimedia Broadcast (DTMB) signals and assisted INS components as external radiation sources for system design. The trigonometric geometry algorithm is proposed to estimate the pseudo-measurement, and the impact factors of the positioning error are analyzed. After filtering the pseudo-measurement by multi-point initiation, we designed a model for cross-regional positioning scenarios using the nearest-neighbor navigation association and scalar weighted distributed fusion. The simulation results demonstrate that the model can effectively track the target. Finally, the effectiveness of the positioning at a constant altitude is evaluated through different vehicle-mounted scenarios with a speed of 60 km/h. The experimental results show that the minimum positioning error can reach 18.95 m over a 525 m trajectory, thus meeting actual UAV requirements and having practical value.