Computation Reduction for Angle of Arrival Estimation Based on Interferometer Principle
Bibliographic record
Abstract
Advancement in wireless technology and the oncoming of the Internet of Things (IoT) marked an incredible growth in the wireless connectivity, ultimately concluding to a major expansion in the mobile electronics industry. Today, around 3.1 billion users are reported being connected to the internet, along with 16.3 billion mobile electronic devices. The increasing connectivity has lead to an increase in demand for mobile services, consequently, increasing demand for location services and mobility analytics. The most common location tracking or direction finding devices are found in the form of Global Positioning System (GPS) which provides location data for a client device using satellites-based lateration techniques. However, the use of the GPS is fairly limited to large distances and often tend to fail when smaller distances are concerned. This thesis aims to dive into the study of different direction finding algorithms based on angle of arrival estimation specifically pertaining to the indoor location tracking and navigation, also known as hyperlocation. The thesis will go over the main elements used in direction finding systems while looking at some of the present research done in this respective field of interest. Afterwards, the thesis will focus on a specific angle of arrival estimation algorithm which is widely being used for hyerplocation solutions and propose an alteration in the algorithm in order to achieve a faster runtime performance on weaker processors. A comparison between the accuracies will be made between the original algorithm and the suggested solution, followed by a runtime comparison on different processing units.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".