Robust Recursive RSSD Based Source Localization in Gaussian Mixture Channels
Bibliographic record
Abstract
Signal strength based localization methods are of great interest due to their low cost and simple implementation. In this letter, a received signal strength difference (RSSD) based approach is presented to localize a source with unknown transmit power. A robust two stage estimator is proposed. First, an RSSD based optimization problem is formulated in the presence of Gaussian mixture measurement noise based on Huber's minimax model. This yields a nonlinear problem which can be approximated by Taylor series expansion. Then, a robust recursive least squares (RRLS) method is developed to provide a robust solution of the approximated problem. The corresponding Cramér-Rao lower bound (CRLB) for correlated Gaussian mixture noise is also derived as a performance benchmark. Results are presented which show that the RRLS method attains the CRLB for a sufficiently large signal to noise ratio (SNR).
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".