RF/microwave system high-fidelity modeling and simulation: application to airborne multi-channel receiver system for angle of arrival estimation
Bibliographic record
Abstract
In this paper, a high-fidelity RF modeling and simulation framework is demonstrated to model an airborne multi-channel receiver system that is used to estimate the angle of arrival (AoA) of received signals from a stationary emitter. The framework is based on System Tool Kit (STK®), Matlab and SystemVue®. The SystemVue-based multi-channel receiver estimates the AoA of incoming signals using adjacent channel amplitude and phase comparisons, and it estimates the Doppler frequency shift of the aircraft by processing the transmitted and received signals. The estimated AoA and Doppler frequency are compared with the ground-truth data provided by STK to validate the efficacy of the modeling process. Unlike other current RF electronic warfare simulation frameworks, the received signal described herein is formed using the received power, the propagation delay and the transmitted waveform, and does not require information such as Doppler frequency shift or radial velocity of the moving platform from the scenario; hence, the simulation is more computationally efficient. In addition, to further reduce the overall modeling and simulation time, since the high-fidelity model computation is costly, the high-fidelity electronic system model is evoked only when the received power is higher than a predetermined threshold.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".