SqueezeNet-Based Range, Angle, and Doppler Estimation for Automotive MIMO Radar Systems
Bibliographic record
Abstract
The frequency modulated continuous waveform multiple - input multiple - output (FMCW MIMO) radar is of great interest to the automotive industry that provides high-end automobiles equipped with parking assistance, lane departure warning, and adaptive cruise control. These radars can simultaneously detect the range, angle, and doppler of the surrounding objects, such as cars, trucks, bicycles, and pedestrians and relay this information to the central control to provide a safe and collision-free cruise control for the self-driving vehicle. The traditional approach for the radar range, angle, and Doppler estimations is the Fast Fourier Transform (FFT)FFT which is computationally efficient but suffers from poor angular resolution. On the other hand, high-resolution techniques such as the Multiple SIgnal Classifier (MUSIC), the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), and the Minimum Variance Distortionless Response (MVDR) can achieve more accurate estimations but are computationally expensive. Moreover, these high-resolution techniques are very sensitive to clutter and interferences and cannot effectively distinguish targets from clutters in such an environment. In this paper, we propose a deep-learning- based FMCW MIMO radar in which the range, angle, and Doppler estimation are treated as a multilabel classification problem. The deep - learning approach is based on the SqueezeNet transfer learning approach to overcome the limitations on the amount of training data and training time. Simulation results demonstrate that the proposed approach outperforms the MVDR method in the presence of clusters and jammers and can achieve a high angular resolution of 2 degrees.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".