Coarray Tensor-Based Angle Estimation for Bistatic MIMO Radar With a Dilated Moving Receive Array
Bibliographic record
Abstract
Utilizing sparse arrays is a very effective and commonly used method to enhance the degrees of freedom (DOFs) of multiple-input multiple-output (MIMO) radar. Unfortunately, as research on sparse arrays has matured, it has become difficult to greatly improve the DOFs by relying on array structure design only. Moreover, the existing angle estimation methods for sparse MIMO radar would process data under a matrix-based framework rather than the entire coarray tensor, thus suffering some loss in angle estimation performance. In this article, we extend the DOFs of MIMO radar by exploiting sparse array motion and propose an angle estimation method exploiting coarray tensor. First, we not only use sparse arrays at the transmitter and receiver parts of MIMO radar but also dilate the interelement spacing of the receive array on a moving platform. We set the transmitted signal as periodic, and further expand the DOFs and virtual aperture of MIMO radar by using the aperture synthesis technique introduced by array motion. Second, we build a self-correlation tensor model and reshape it to produce an optimal tensor with the highest DOFs that can be obtained under the uniqueness condition of parallel factor decomposition. Third, we theoretically analyze the achievable DOFs of the proposed method and show that the maximum number of detectable targets of bistatic MIMO radar can be increased to about three times. Simulation results verify the correctness of the theoretical analysis and demonstrate the superior estimation performance of our proposed method.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".