A ML-Based Framework for Joint TOA/AOA Estimation of UWB Pulses in Dense Multipath Environments
Bibliographic record
Abstract
We present a joint estimator of the time of arrival (TOA) and angle of arrival (AOA) for impulse radio ultrawideband (UWB) systems in which an antenna array is employed at the receiver. The proposed method consists of two steps: 1) preliminary estimation of the TOA and the average power delay profile (APDP) using energy-based threshold crossing and log-domain least-squares fitting, respectively; and 2) joint TOA refinement and AOA estimation by local 2-D maximization of a log-likelihood function (LLF) that employs the preliminary estimates from the first step. The derivation of the LLF relies on an original formulation in which the superposition of images from secondary paths is modeled as a Gaussian random process, whose second-order statistical properties are characterized by a wideband space-time correlation function. In addition to the APDP, this function incorporates a special gating mechanism to represent the onset of the secondary paths, thereby leading to a novel form of the LLF. Closed-form expressions for the Cramer-Rao bound on the variance of the TOA and AOA estimators are also derived, which formally take into account pulse overlap through this gating mechanism. In simulation experiments based on multipath UWB channel models featuring both diffuse and directional image fields, our approach exhibits superior performance to that of a competing scheme from the recent literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".