Constrained Passive Sensor Placement for Bearings-Only Tracking under Uncertainty

Date

2026-09-02

Author

Güneş, Melike
Yaşar, Hüseyin Avni

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

22
views

0
downloads

Cite This

We study constrained sensor placement for target localization and tracking with passive angular measurements. Sensors are selected from a discrete candidate set under minimum-separation and keep-out constraints. Placement quality is evaluated via an Extended Kalman Filter (EKF) covariance objective over a Monte Carlo ensemble of target trajectories, using Joseph-form updates for numerical stability. We propose a computationally efficient greedy selection scheme with geometric pre-screening, and benchmark it against random placement, uniform-circle placement, K-means, and a discrete genetic algorithm. Across M=50 scenarios at σθ=2° and N=4 sensors, greedy achieves the lowest mean uncertainty objective (e.g., 650.7 ± 27.8 vs. 689.6 ± 25.1 for K-means) while requiring substantially fewer objective evaluations. Ablations over the screening budget K′ show a clear quality–runtime trade-off, and a robust multi-regime design improves worst-case performance under higher bearing noise. Finally, extending the measurement model to range–bearing sensing yields additional uncertainty reduction, demonstrating the flexibility of the framework.

URI

https://ieeexplore.ieee.org/document/11672367
https://hdl.handle.net/11511/120945

Collections

Department of Mathematics and Science Education, Conference / Seminar