Enhanced Radar False Alarm Mitigation in Low-RCS Target Detection Using Time-Varying Trajectories on Range–Doppler Diagrams With DCNN
Bibliographic record
Abstract
We propose detecting low-radar cross section (RCS) targets using the time-varying characteristics in the range-Doppler diagram with 3-D deep convolutional neural networks (3D-DCNNs), which significantly suppresses false alarms (FAs). When low-RCS targets are in cluttered environments, it is not easy to detect them because of the tradeoff between the probability of detection (PD) and FA rate depending on the detection threshold. In this article, we employ a 3D-DCNN to observe the trajectory of an object over a certain time and determine whether the detected object is a target or not. We use a constant FA rate (CFAR) to detect low-RCS targets using low thresholds in highly cluttered environments. This approach results in the detection of a significant amount of unwanted clutter and noise. The proposed algorithm effectively suppresses the FA rate and enhances overall detection accuracy. A comparison of the performance through simulation revealed that the probability of FA ($P_{\text {fa}}$) from$3 \times 10^{-3}$with CFAR and density-based spatial clustering of applications with noise (DBSCAN) was reduced to$1\text {.}2 \times 10^{-5}$with the proposed algorithm. For validation, we measured a drone, an example of a low-RCS target, at 10 m using a 77-GHz frequency-modulated continuous-wave (FMCW) radar manufactured by TI.$P_{\text {fa}}$was$3 \times 10^{-3}$when the CFAR and DBSCAN were applied with one and two drones, respectively. However, the proposed algorithm reduced this to$9\text {.}3 \times 10^{-6}$and$2\text {.}1 \times 10^{-5}$, respectively. In addition, a drone was measured and verified using FMCW radar manufactured by TORIS at 7 km. The proposed algorithm reduces$P_{\text {fa}}$from$2\text {.}5 \times 10^{-4}$to$7\text {.}4 \times 10^{-7}$.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".