Coefficient Quantization and Enhanced Spectral Interpolation for Resource-Constrained Doppler Processing on FPGA
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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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".