Angle Estimation for Bistatic MIMO Radar With a Sparse Moving Array in the Presence of Position Errors and Gain-Phase Perturbation
Bibliographic record
Abstract
We recently extended the degrees of freedom for the bistatic multiple-input multiple-output (MIMO) radar by exploiting sparse array motion at the receiver part, and considered the sensor position errors arising from array motion. However, this technique does not consider the sensor position errors along the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$x$</tex-math></inline-formula> -axis and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$y$</tex-math></inline-formula> -axis, nor the gain-phase errors of the sparse moving array. There are also spurious results during the angle estimation. This article extends the one-dimensional sensor position errors to the case of two-dimensional errors generated by the array motion and considers the gain-phase perturbation of the sparse moving array, which is inevitable on the moving platform. We first use a dedicated calibration source to estimate the gain-phase errors and compensate for the received data to obtain accurate angle estimates. We get the angle estimates by two approaches: one relies on the calibration source, and the other resorts to self-calibration processing. We also introduce an unfolded coprime linear array at the receiver part, which avoids spurious results and increases the array aperture. The ambiguous solutions of the proposed methods are theoretically analyzed, and the Cramér-Rao bound of angle estimate errors in the presence of sensor position errors is derived. Finally, numerous simulation results show that our methods can achieve superior estimation performance under the aforementioned errors.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".