Adaptable Electrically Steerable Antenna Array with Diverse Switchable Polarization
Bibliographic record
Abstract
Adaptability is essential for antenna systems, especially those operating in harsh or unpredictable environments.Adaptable electrically steerable antennas (AESA) provide this flexibility.AESA use in radar systems is expanding due to new technologies that enable higher power and more miniaturized implementations.Polarization diversity is another way of adding adaptability to radar systems.System-level analysis of a typical radar system was performed to determine the specifications for a series of 4x4 switchable polarization AESA arrays meant to operate from 9.4 to 9.5 GHz.These arrays were designed on a multilayer Rogers Duroid PCB.Sub-components such as a microstrip feeding network, patch antennas, switches and hybrid couplers were integrated on the same PCB utilizing both sides of the board.Simple 4x4 linear and 4x4 circular polarized arrays were designed and fabricated on this chosen package.These single-polarization arrays were compacted into a single lowprofile PCB that measures 2.5x2.5 inches.The measurements of both single-polarization arrays closely matched the simulated results, which helped correct issues with the design process before adding more polarization states.Measurements show that these modularly scalable 2.5x2.5-inchPCBs can achieve a gain of between 12 and 21 dB depending on how many boards are tiled together.A technique for increasing the bandwidth of a single patch antenna through resistive loading was also explored.Using commercially packaged resistors to load a single patch resulted in a bandwidth increase of 400 MHz.Once simple single-polarization arrays were verified, two switchable polarization arrays, one linear and one circular, were designed and simulated.These switchable The professors at the Department of Electronics at Carleton University challenged me in unique ways.Those who pushed the hardest helped prove that I can accomplish great things if I stay focused, have a plan and trust my abilities.My supervisor and mentor, Rony E. Amaya, whose guidance has expanded my skillset and opened up many research and work opportunities.My high school physics teacher Eric Lorenzen whose passion for science inspired me to pursue engineering as a career. My mother, Elizabeth Clark
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.003 | 0.002 |
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".