Coefficient Quantization and Enhanced Spectral Interpolation for Resource-Constrained Doppler Processing on FPGA
Why this work is in the frame
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Bibliographic record
Abstract
Accurate Doppler frequency detection is fundamental to radar systems for determining target velocity, yet a persistent trade-off exists between estimation accuracy and the computational resources required for real-time implementation, particularly on hard-ware-limited platforms like FPGAs. This study presents a novel filter bank architecture designed to resolve this conflict by employing two key methodological innovations: the strategic optimization of filter coefficient bit-width to minimize hardware resource consumption, and the development of an enhanced two-stage frequency estimation algorithm that interpolates the target Doppler shift from the outputs of adjacent filters. Simulation results and hardware implementation on a Xilinx Artix-7 FPGA demonstrate that the proposed design maintains high detection accuracy, with a stopband attenuation exceeding 50 dB and less than 0.05 dB passband deviation and reliably identifies multiple targets in low signal-to-noise ratio (SNR) environments of -20 dB, while concurrently reducing DSP block utilization by 40%. We conclude that this architecture provides a solution for high-fidelity Doppler detection in resource-constrained real-time radar applications, effectively balancing the demands of precision and efficiency.
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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