High-Accuracy and Robust Non-Cooperative AAV Localization: RSS-Based Framework With Unknown Transmission Power

Authors: Wang, F., Li, Q., Li, X., Zhang, J., Zeng, M., Niyato, D., Nallanathan, A., Yuen, C.

Journal: IEEE Transactions on Communications

Publication Date: 01/01/2026

Volume: 74

Pages: 8763-8780

eISSN: 1558-0857

ISSN: 0090-6778

DOI: 10.1109/TCOMM.2026.3692108

Abstract:

This paper proposes a robust received signal strength (RSS)-based localization framework for non-cooperative autonomous aerial vehicles. Conventional RSS methods face three fundamental obstacles: susceptibility to heavy-tailed measurement noise, intractable non-convexity, and severe accuracy degradation when target transmission power is unknown. These vulnerabilities present critical security risks to emerging low-altitude economy networks. To overcome these limitations, we propose an integrated joint-estimation architecture. First, a cascaded preprocessing pipeline, combining Gaussian outlier suppression and statistical median weighting, is developed to mitigate multipath-induced biases and minimize variance. Second, an information-theoretic base station (BS) selection mechanism is designed to identify geometrically optimal BSs, thereby exponentially reducing computational overhead in both uniform and random deployment scenarios. Third, the power-unknown problem is reformulated via semidefinite programming, absorbing the unknown parameter into a higher-dimensional convex cone to guarantee global convergence without relying on initial guesses. Extensive Monte Carlo simulations demonstrate that under uniform BS deployment, our strategy achieves sub-10-meter accuracy (approximately 5 m root mean square error) using only 5 selected BSs in typical urban conditions with a path loss exponent of 3. Consequently, this approach delivers a highly accurate and computationally efficient solution for real-time target tracking in complex environments.

https://eprints.bournemouth.ac.uk/41944/

Source: Scopus