Harmonic Retrieval Joint Multiple Regression: Robust DOA Estimation for FMCW Radar in the Presence of Unknown Spatially Colored Noise
Bibliographic record
Abstract
In this letter, we present a direction-of-arrival (DOA) estimation algorithm named Harmonic Retrieval joint Multiple Regression (HRMR) for FMCW radar against unknown spatially colored noise. The HRMR algorithm focuses on the direct data domain. First, the target's IF signal is resolved and reconstructed by the selected harmonic retrieval method. Then, we regard the signal expression of each array element as an independent form of multiple regression and further achieve the DOA by iteratively estimating the regression coefficient of the reconstructed target's IF signal from each array element one at a time. By exploiting the existence of the same noise distribution on each element whether the noise is spatially colored or Gaussian white (i.e., temporally and spatially white), the HRMR algorithm eliminates the negative effect of the spatially colored noise and demonstrates robustness. In addition, the impact of the poles of the spatially colored noise is displayed by experiments. Furthermore, we validate that the number of saturated targets of HRMR algorithm is not restricted by the number of array elements in simulations.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".