Precise GNSS Attitude Determination Based on Antenna Array Processing
Bibliographic record
Abstract
Global Navigation Satellite Systems (GNSS)-based attitude determination has been recognized as a significant field of study in numerous ground, marine and airborne applications. This paper investigates the feasibility of precise attitude determination using a GNSS receiver capable of antenna array processing. The ability to determine attitude parameters by employing only a single satellite signal distinguishes this approach from methods based on carrier phase measurements and ambiguity resolution. This is especially important in challenging environments where a limited number of satellites are available. Moreover, this approach has the advantage over other methods by employing an antenna array with short spacing between adjacent antennas (less than half a wavelength), especially where the structural dimension is an important concern such as in small unmanned aerial vehicles (UAV). Herein, a modified version of the recursive least squares (RLS) method is proposed to adaptively estimate each satellite’s steering vector and then the roll, pitch and heading angles of a moving vehicle. The proposed adaptive method is fast and computationally of low complexity and therefore it can properly operate in real time applications. The proposed method is applied to a set of real GPS L1 signals collected using a six-element antenna array to verify its effectiveness and assess its performance. A tactical-grade inertial navigation sensor (INS) is used as reference to evaluate the accuracy of heading estimates in a scenario where the array is mounted on a moving vehicle.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".