JK
J.H.J. Kuipers
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This work surveys and evaluates passive RF-based Signals of Opportunity (SoOP) for UAV self-localization in GNSS-denied environments. We review Time Difference of Arrival (TDoA), Angle of Arrival (AoA), Doppler shift, and Received Signal Strength Indicator (RSSI) measurement techniques, together with the state estimation frameworks used to process them. To evaluate these trade-offs in practice, a MATLAB simulation framework is developed using ray-traced urban channel models for TU Delft and Chicago, and a free-space open-field model. Three Extended Kalman Filter variants are compared across these environments: TDoA-only, RSSI-only, and fused TDoA+RSSI. Results show that TDoA consistently outperforms RSSI in all scenarios, while the fused filter yields improvements of up to 55% over TDoA alone in challenging NLOS configurations. The dominant factor governing localization accuracy is transmitter geometry and density rather than propagation model fidelity, and hybrid approaches are most beneficial precisely when single-modality observability is poor.
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This work surveys and evaluates passive RF-based Signals of Opportunity (SoOP) for UAV self-localization in GNSS-denied environments. We review Time Difference of Arrival (TDoA), Angle of Arrival (AoA), Doppler shift, and Received Signal Strength Indicator (RSSI) measurement techniques, together with the state estimation frameworks used to process them. To evaluate these trade-offs in practice, a MATLAB simulation framework is developed using ray-traced urban channel models for TU Delft and Chicago, and a free-space open-field model. Three Extended Kalman Filter variants are compared across these environments: TDoA-only, RSSI-only, and fused TDoA+RSSI. Results show that TDoA consistently outperforms RSSI in all scenarios, while the fused filter yields improvements of up to 55% over TDoA alone in challenging NLOS configurations. The dominant factor governing localization accuracy is transmitter geometry and density rather than propagation model fidelity, and hybrid approaches are most beneficial precisely when single-modality observability is poor.