A Low Complexity STPAP Algorithm based on Alternating Polarization Sensitive Array
Bibliographic record
Abstract
Abstract Background : Space-time adaptive processing (STAP) has been widely used in the fields of communication, radar, and navigation anti-jamming. However, the traditional scalar array used by STAP has certain limitations，because it can only obtain spatial information. In order to further improve the performance of the space-time domain joint filtering technology, this paper replaces the traditional scalar array with an alternating polarization sensitive array (APSA). Compared with the dual polarization sensitive array (DPSA), it can not only obtain the polarization information of the signal, but also reduce the computational complexity of the algorithm. Methods : Using the polarization information of the signals, this paper realizes an alternate polarization sensitive array space-time-polarization adaptive processing algorithm (APSA-STPAP) based on the linear variance minimum criterion (LCMV). Different from the traditional LCMV criterion, this paper takes the space-time polarization joint steering vector of the desired signal and the interference signal as the constraint matrix, and uses the "1 condition" and "zero condition" as the constraint conditions to effectively suppress the interference signal and enhance the expectation signal. Results : The simulation results show that: (1) APSA-STPAP algorithm can achieve the same filtering effect as DPSA-STPAP algorithm. From the perspective of the spatial domain, time domain and polarization domain, it can form null in the direction of interference, effectively suppress the interference signal, and realize space-time-polarization adaptive processing. (2) Under the same conditions, APSA-STPAP algorithm can achieve the same filtering effect as DPSA-STPAP algorithm. there is a big difference between the two algorithms, APSA-STPAP algorithm can effectively reduce the amount of computation. Moreover, the dipole of alternating polarization sensitive array is halved, which reduces the coupling effect between electric dipoles, and is conducive to engineering implementation. (3) APSA-STPAP algorithm can maintain good anti-interference performance even when the electric dipole and anti-jamming degree of freedom are reduced by half, and its anti-jamming performance is similar to that of polarization sensitive array. The output SINR of DPSA-STPAP algorithm is about 3dB higher than that of APSA-STPAP algorithm. There is little difference between the anti- interference performance of APSA and DPSA.
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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 | 0.001 | 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 | 0.002 |
| 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".