Long Range Surveillance MIMO Radar at 24 GHz
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, radar-based detection and tracking at millimeter-wave frequencies have become increasingly popular. However, the performance of the radar systems operating at millimeter-wave frequencies has been mainly limited to short ranges which makes them unsuitable for many practical applications. In this paper, long range detection and tracking for multiple targets at millimeter-wave frequencies is presented based on our custom-made Multiple Input Multiple Output (MIMO) radar system. The structure of the radar and the innovation in the digital signal processing unit will make it possible to perform detection and tracking for targets at far ranges. To the best of our knowledge, this is the first millimeter-wave system which is capable of detecting and tracking human at 600 m radial distance from the radar system. We present a complete statistical analysis of the combined land clutter and the noise of the system. This paves the way for the implementation of the Constant False Alarm Rate (CFAR) algorithm to detect the desired targets. Following the detection block the multi-target tracking is performed. The tracking unit is implemented based on the Kalman filtering and constant velocity motion model while the data association is based on the state-of-the-art Joint Probabilistic Data Association Filter (JPDAF). The algorithms have been implemented using the C programming language, and fully real-time processing has been achieved. The results presented in the paper are based on the experimental data gathered from realistic scenarios which demonstrate the capability of the radar system, which operates at 24 GHz, as well as the algorithms,
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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 | 0.000 |
| 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) | 0.001 | 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 it