Metaheuristic Optimization for Robust RSSD-Based UAV Localization with Position Uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) have garnered significant research interest across various fields due to their excellent maneuverability, scalability, and flexibility. However, potential collisions and other issues can disrupt communication and hinder functionality in real-world applications. Therefore, accurate localization of UAVs is crucial. Nonetheless, environmental factors and inherent stability issues can lead to node positional errors in UAV networks, compounded by inaccuracies in transmit power estimation, complicating the effectiveness of signal strength-based localization methods in achieving high accuracy. To mitigate the adverse effects of these issues, a novel received signal strength difference (RSSD)-based localization scheme based on a robust enhanced salp swarm algorithm (RESSA) is presented. In this algorithm, an elitism strategy based on tent opposition-based learning (TOL) is proposed to promote the leader to move around the food source. Differential evolution (DE) is then used to enhance the exploration ability of each agent and improve global search. In addition, a dynamic movement mechanism for followers is designed, enabling the swarm to swiftly converge towards the food source, thereby accelerating the overall convergence process. The RSSD-based Cramér–Rao lower bound (CRLB) with position uncertainty is derived to evaluate the performance. Experimental results are presented, which show that the proposed RESSA provides better localization performance than related methods in the literature.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it