Mitigating Direct Current (DC) Offset Impairments in Zero-IF Receivers for Enhanced Radar and Remote Sensing Applications
Bibliographic record
Abstract
Wideband spectrum sensing is a critical function for radar warning receivers, electronic warfare (EW) systems, and RF situational awareness platforms. This paper presents a mathematical model and mitigation strategy for DC offset spikes, a common artifact in sequential multiband spectrum sensing (SMSS) using low-cost software-defined radios (SDRs) with direct-conversion receiver (DCR) or zero-intermediate frequency (Zero-IF) architectures. These impairments, stemming from local oscillator (LO) leakage and I/Q imbalance, introduce narrowband spectral spikes that significantly elevate the noise floor, degrade the signal-to-noise ratio (SNR), and can mask weak targets or signals in radar and remote sensing applications. We model these spikes using Fourier theory and evaluate the efficacy of several mitigation strategies, including DC offset calibration. Results from both simulations and practical experiments using a HackRF SDR show that a simple mean subtraction calibration can reduce these detrimental spikes by up to 50 dB, dramatically improving dynamic range and signal detection capability without compromising signal integrity. This work provides a practical, low-complexity solution to a fundamental hardware impairment, enhancing the performance of affordable SDRs for critical sensing systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".