FK
F.L. Kosterhon
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
5 records found
1
This work investigates an airborne (UAV-based) multistatic passive radar using signals of opportunity in the context of a GNSS-denied environment. Surveyed works on multistatic passive radars either assume zero receiver positioning error, or otherwise ignore its heterogeneity across receivers, which we argue is expected in GNSS-denied navigation due to varying conditions. Rui and Ho’s error model accommodates heterogeneous per-receiver errors but is evaluated only for the IID case. We adapt this model and study the heterogeneous regime it permits with a weighted least-squares (WLS) solver, and operationalise their deferred drifting-receiver case via an EKF tracker with per-receiver drift state. We run a Monte Carlo simulation with 10,000 trials per configuration varying the number of receivers and receiver positioning error parameters to compare a per-receiver-error-aware solver against a solver assuming IID error across receivers, and a solver assuming no self-positioning error at all. The findings show that, relative to the IID solver, the per-receiver-aware solver reduces the median target-position error by ∼13% in snapshot WLS (at N =10, σRx =5 m) and the median steady-state target-position RMSE by ∼28% in EKF tracking (at N =10, σdrift=0.1 m/√s). This suggests that in a UAV-based multistatic passive radar, per-receiver self-positioning error is a non-negligible parameter to consider in design.
...
This work investigates an airborne (UAV-based) multistatic passive radar using signals of opportunity in the context of a GNSS-denied environment. Surveyed works on multistatic passive radars either assume zero receiver positioning error, or otherwise ignore its heterogeneity across receivers, which we argue is expected in GNSS-denied navigation due to varying conditions. Rui and Ho’s error model accommodates heterogeneous per-receiver errors but is evaluated only for the IID case. We adapt this model and study the heterogeneous regime it permits with a weighted least-squares (WLS) solver, and operationalise their deferred drifting-receiver case via an EKF tracker with per-receiver drift state. We run a Monte Carlo simulation with 10,000 trials per configuration varying the number of receivers and receiver positioning error parameters to compare a per-receiver-error-aware solver against a solver assuming IID error across receivers, and a solver assuming no self-positioning error at all. The findings show that, relative to the IID solver, the per-receiver-aware solver reduces the median target-position error by ∼13% in snapshot WLS (at N =10, σRx =5 m) and the median steady-state target-position RMSE by ∼28% in EKF tracking (at N =10, σdrift=0.1 m/√s). This suggests that in a UAV-based multistatic passive radar, per-receiver self-positioning error is a non-negligible parameter to consider in design.
Reliable positioning is essential for autonomous drones, yet global navigation satellite systems (GNSS) can become unreliable or unavailable. This work compares passive RF-based methods for estimating distance and relative radial velocity with a single unsynchronized receiver. Calibrated RSSI ranging and direct Doppler estimation are selected as lightweight approaches and evaluated using an RFSoC 4x2-based prototype with a continuous-wave signal. Under controlled indoor conditions, calibrated RSSI achieves a mean absolute percentage error of 6.3% over the tested positions, although antenna orientation changes RSSI by almost 7 dB at a fixed distance. Doppler estimation, with clock offset estimated from stationary measurements before and after motion, recovers the correct motion direction in all motion trials and achieves a mean absolute error of 0.33 m/s. The motion-trial RMSE and the stationary null-test standard deviation were both 0.43 m/s, suggesting that imperfect clock-offset compensation and estimator uncertainty account for a substantial part of the radial-velocity estimation error. The results show that both methods provide lightweight distance and radial-velocity features, but their accuracy is limited by propagation conditions and oscillator stability.
...
Reliable positioning is essential for autonomous drones, yet global navigation satellite systems (GNSS) can become unreliable or unavailable. This work compares passive RF-based methods for estimating distance and relative radial velocity with a single unsynchronized receiver. Calibrated RSSI ranging and direct Doppler estimation are selected as lightweight approaches and evaluated using an RFSoC 4x2-based prototype with a continuous-wave signal. Under controlled indoor conditions, calibrated RSSI achieves a mean absolute percentage error of 6.3% over the tested positions, although antenna orientation changes RSSI by almost 7 dB at a fixed distance. Doppler estimation, with clock offset estimated from stationary measurements before and after motion, recovers the correct motion direction in all motion trials and achieves a mean absolute error of 0.33 m/s. The motion-trial RMSE and the stationary null-test standard deviation were both 0.43 m/s, suggesting that imperfect clock-offset compensation and estimator uncertainty account for a substantial part of the radial-velocity estimation error. The results show that both methods provide lightweight distance and radial-velocity features, but their accuracy is limited by propagation conditions and oscillator stability.
Unmanned Aerial Vehicles (UAVs) operating in GNSS-denied environments typically rely on Inertial Measurement Units (IMUs) for position estimation. However, this approach is susceptible to error accumulation, commonly known as inertial drift. Standard industry solutions mitigate this issue by fusing IMU data with external sensors such as LiDAR or cameras. However, these sensing modalities are not suitable for all environments. An alternative approach is to leverage cooperation within a swarm of drones, enabling agents to exchange information and improve their position estimates collectively. One such method employs a Distributed Graph Optimization (DGO) algorithm to cross-reference spatial uncertainties among UAVs in the swarm. However, existing DGO frameworks are primarily validated using relative swarm cohesion metrics, which provide little insight into the swarm's absolute positioning accuracy.
To address this limitation, this paper evaluates a basic DGO state estimation model against a basic Dead Reckoning (DR) baseline. A Python-based simulation environment was developed, and four experimental conditions were investigated: varying sensor quality, swarm size, flight duration, and trajectory geometry. The results show that DGO outperforms DR under degraded sensor conditions, whereas DR maintains lower error during short-duration flights when high-quality sensors are available. Crucially, a temporal breakeven point is identified beyond which the unbounded error growth of DR exceeds that of the cooperative DGO framework. This finding demonstrates that while standalone DR offers superior short-term precision, cooperative estimation provides a more stable and sustainable framework for prolonged operations in GNSS-denied environments. ...
To address this limitation, this paper evaluates a basic DGO state estimation model against a basic Dead Reckoning (DR) baseline. A Python-based simulation environment was developed, and four experimental conditions were investigated: varying sensor quality, swarm size, flight duration, and trajectory geometry. The results show that DGO outperforms DR under degraded sensor conditions, whereas DR maintains lower error during short-duration flights when high-quality sensors are available. Crucially, a temporal breakeven point is identified beyond which the unbounded error growth of DR exceeds that of the cooperative DGO framework. This finding demonstrates that while standalone DR offers superior short-term precision, cooperative estimation provides a more stable and sustainable framework for prolonged operations in GNSS-denied environments. ...
Unmanned Aerial Vehicles (UAVs) operating in GNSS-denied environments typically rely on Inertial Measurement Units (IMUs) for position estimation. However, this approach is susceptible to error accumulation, commonly known as inertial drift. Standard industry solutions mitigate this issue by fusing IMU data with external sensors such as LiDAR or cameras. However, these sensing modalities are not suitable for all environments. An alternative approach is to leverage cooperation within a swarm of drones, enabling agents to exchange information and improve their position estimates collectively. One such method employs a Distributed Graph Optimization (DGO) algorithm to cross-reference spatial uncertainties among UAVs in the swarm. However, existing DGO frameworks are primarily validated using relative swarm cohesion metrics, which provide little insight into the swarm's absolute positioning accuracy.
To address this limitation, this paper evaluates a basic DGO state estimation model against a basic Dead Reckoning (DR) baseline. A Python-based simulation environment was developed, and four experimental conditions were investigated: varying sensor quality, swarm size, flight duration, and trajectory geometry. The results show that DGO outperforms DR under degraded sensor conditions, whereas DR maintains lower error during short-duration flights when high-quality sensors are available. Crucially, a temporal breakeven point is identified beyond which the unbounded error growth of DR exceeds that of the cooperative DGO framework. This finding demonstrates that while standalone DR offers superior short-term precision, cooperative estimation provides a more stable and sustainable framework for prolonged operations in GNSS-denied environments.
To address this limitation, this paper evaluates a basic DGO state estimation model against a basic Dead Reckoning (DR) baseline. A Python-based simulation environment was developed, and four experimental conditions were investigated: varying sensor quality, swarm size, flight duration, and trajectory geometry. The results show that DGO outperforms DR under degraded sensor conditions, whereas DR maintains lower error during short-duration flights when high-quality sensors are available. Crucially, a temporal breakeven point is identified beyond which the unbounded error growth of DR exceeds that of the cooperative DGO framework. This finding demonstrates that while standalone DR offers superior short-term precision, cooperative estimation provides a more stable and sustainable framework for prolonged operations in GNSS-denied environments.
Reliable UAV navigation in GNSS-challenged environments often requires complementary sensing methods that can operate when satellite positioning is degraded or unavailable. Radio-frequency signals of opportunity can support this type of navigation, but their practical use depends on intermediate processing stages such as direction-of-arrival estimation. This work compares three FPGA-oriented DoA estimation approaches, MUSIC, ESPRIT, and an LU-decomposition-based estimator, within a common RFSoC 4x2 and Vitis HLS implementation framework. Instead of proposing a new estimator, the study evaluates existing hardware-oriented methods under shared assumptions about the receiver, array, fixed-point, signal model, and measurements. All methods use the same four-element ULA configuration, broadside angle convention, 256-snapshot frame structure, and captured RF data. The evaluation compares angular accuracy, HLS kernel latency, update interval, and FPGA resource usage. MUSIC provides the strongest full-range angular robustness, while the LU-based estimator gives the lowest latency and resource cost but produces larger edge-angle error tails. ESPRIT provides intermediate accuracy while retaining timing and resource characteristics close to those of MUSIC, since both methods rely on EVD-based subspace processing.
...
Reliable UAV navigation in GNSS-challenged environments often requires complementary sensing methods that can operate when satellite positioning is degraded or unavailable. Radio-frequency signals of opportunity can support this type of navigation, but their practical use depends on intermediate processing stages such as direction-of-arrival estimation. This work compares three FPGA-oriented DoA estimation approaches, MUSIC, ESPRIT, and an LU-decomposition-based estimator, within a common RFSoC 4x2 and Vitis HLS implementation framework. Instead of proposing a new estimator, the study evaluates existing hardware-oriented methods under shared assumptions about the receiver, array, fixed-point, signal model, and measurements. All methods use the same four-element ULA configuration, broadside angle convention, 256-snapshot frame structure, and captured RF data. The evaluation compares angular accuracy, HLS kernel latency, update interval, and FPGA resource usage. MUSIC provides the strongest full-range angular robustness, while the LU-based estimator gives the lowest latency and resource cost but produces larger edge-angle error tails. ESPRIT provides intermediate accuracy while retaining timing and resource characteristics close to those of MUSIC, since both methods rely on EVD-based subspace processing.
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.
...
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.