Acquisition of Weak Signals in Multi-Constellation Frequency Domain Receivers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
New positioning applications’ availability requirements demand receivers with higher sensitivities and ability to process multiple GNSS signals. Possible applications include acquiring one signal per GNSS constellation in the same frequency band and combining them for increased sensitivity or predicting acquisition of other signals. Frequency domain processing can be used for this purpose, since it benefits from parallel processing capabilities of Fast Fourier Transform (FFT), which can be efficiently implemented in software receivers. On the other hand, long coherent integration times are mainly limited due to large FFT size in receivers using frequency domain techniques. A new method is proposed to address the problems in frequency domain receivers without compromising the resources and execution time. A pre-correlation accumulation (PCA) is proposed to partition the received samples into one-code-period blocks, and to sum them together. As a result, the noise is averaged out and the correlation results will gain more power, provided that the relative phase between the data segments is compensated for. In addition to simplicity, the proposed PCA method enables the use of one-size FFT for all integration times. A post-correlation peak combination is also proposed to remove the need for double buffering. The proposed methods are implemented in a configurable Simulink model, developed for acquiring recorded GNSS signals. For weak signal scenarios, a Spirent GPS simulator is used as a source. Acquisition results for GPS L1 C/A and GLONASS L1OF are shown and the performance of the proposed technique is discussed. The proposed techniques target GNSS receivers using frequency domain processing aiming at accommodating all the GNSS signals, while minimizing resource usage. They also apply to weak signal acquisition in frequency domain to answer the availability demand of today’s GNSS positioning applications.
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.
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 it