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 (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{\text {fa}}$ </tex-math></inline-formula>) from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3 \times 10^{-3}$ </tex-math></inline-formula> with CFAR and density-based spatial clustering of applications with noise (DBSCAN) was reduced to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\text {.}2 \times 10^{-5}$ </tex-math></inline-formula> 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. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{\text {fa}}$ </tex-math></inline-formula> was <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3 \times 10^{-3}$ </tex-math></inline-formula> when the CFAR and DBSCAN were applied with one and two drones, respectively. However, the proposed algorithm reduced this to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9\text {.}3 \times 10^{-6}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\text {.}1 \times 10^{-5}$ </tex-math></inline-formula>, respectively. In addition, a drone was measured and verified using FMCW radar manufactured by TORIS at 7 km. The proposed algorithm reduces <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{\text {fa}}$ </tex-math></inline-formula> from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\text {.}5 \times 10^{-4}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7\text {.}4 \times 10^{-7}$ </tex-math></inline-formula>.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".