Adapting Object Detection Models for Multi-Target Detection Utilizing Radars
Bibliographic record
Abstract
This paper investigates the use of Deep Learning (DL) in multiple input multiple output (MIMO) radar target detection, focusing on azimuth and elevation estimation. Traditional methods face challenges like interference and reflections, especially in multi-target scenarios. Feature extraction conventionally relies on range correlation, Doppler filtering, and angle beamforming, followed by detection after constant false alarm rate (CFAR) processing. However, early data sparsification by bin selection often leads to information loss, particularly with large data cubes required for practical implementation. Deep Learning techniques offer an alternative, specifically in azimuth and elevation detection at earlier stages of radar data processing. We developed a convolutional neural network (CNN) model that achieved Mean Square Errors (MSE) of 0.149 for azimuth and 0.168 for elevation on single-target data from 5,000 samples. The model's performance in dual-target scenarios showed MSEs ranging from 0.838 to 1.845, tested on 8,000 samples from a dataset of 72,000. This paper details the model development process, its impact on radar target detection, and potential future research directions involving the substitution of multi-bin Deep Learning blocks with traditional methods.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".