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.
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 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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.002 | 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".