Efficient RSSD-Based Source Positioning with System Parameter Uncertainties
Bibliographic record
Abstract
Passive source location determination is a very active research area. In this paper, we present a received signal strength difference (RSSD) source localization method based on a total least square (TLS) estimator. Due to errors in the data vector and system matrix, the least squares (LS) and weighted least squares (WLS) methods are not applicable as they produce large bias in the location estimation. Therefore, an extension of the LS methods, called total least squares (TLS) is used to solve this problem. Due to the relationship between the data vector and system matrix, a modified TLS method is presented to achieve a closed form estimate. The advantage of this approach is that it does not require transmit power estimation as with other methods, and the complexity is low. The received signal strength difference is used to eliminate uncertainties due to the signal propagation parameters. Performance results are presented which show that the performance of the proposed method comes close to achieving the Cram'er-Rao lower bound.
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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 | 0.000 |
| 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 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".