DOA Estimation for HFSWR Target Based on PSO-ELM
Why this work is in the frame
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Bibliographic record
Abstract
High-frequency surface wave radar (HFSWR) plays an important role in vessel target surveillance. However, HFSWR’s inaccuracy of azimuth estimation caused by wide beams severely limits its detection ability. To solve this problem, a novel direction of arrival (DOA) estimation method based on extreme learning machine optimized by particle swarm optimization (PSO-ELM) is proposed to improve azimuth estimation accuracy for HFSWR. This method can obtain the optimal solution without searching the whole angle range of HFSWR. Specifically, PSO optimizes the input weight and hidden layer bias of ELM to obtain optimal parameters for improving the estimation performance. Based on the optimized parameters, the ELM network can give an optimal azimuth estimation in the sense of least squares and minimal norm. The sample sets used for PSO-ELM training are obtained by matching the points detected by HFSWR with the target points reported by an automatic identification system (AIS) on the range–Doppler (RD) spectra. The performance of DOA estimation is verified by field HFSWR data. The experimental results show that the new method has lower root-mean-square error and higher computational efficiency in comparison to the typical DOA estimation methods, such as digital beam forming (DBF) and multiple signal classification (MUSIC). It also uses the machine learning methods, such as back propagation neural network (BPNN) and support vector regression (SVR).
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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 | 0.000 | 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 it