Design and implementation of dual-channel weak signal acquisition system based on Kalman fusion
Bibliographic record
Abstract
Aiming at the problems that the equipment is difficult to effectively collect underwater magnetic anomaly signal detection, the dynamic range of analog-to-digital converter is limited, resulting in low signal-to-noise ratio, and weak signals are easily submerged by noise. This thesis designs a high-precision magnetic anomaly signal acquisition system based on domestic field-programmable gate array (FPGA). In order to improve the signal-to-noise ratio and dynamic range of the signal, this paper uses the high-precision multi-channel analog-to-digital conversion chip ADS1278 and the Unisplendour Logos series FPGA to collect the weak magnetic anomaly signal output by the three-axis fluxgate after using 2 times and 8 times gain pre-processing respectively, and then uses the Kalman filter to perform secondary correction on the fused data after the average weighted average of the collected signals for preliminary data fusion. The experimental results show that the accuracy of the fused signal is significantly better than that of the single-channel acquisition signal. When using dual-channel AD to acquire weak signals with a frequency of 200 Hz and an amplitude of 500 μV, the signal-to-noise ratio is improved by 26.099 dB, effectively improving the dynamic range of the data acquisition system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".