Enhanced Indoor Navigation System with Beacons and Kalman Filters
Bibliographic record
Abstract
Indoor positioning systems are used in a variety of applications from shopping malls and museums to subject monitoring and tracking. The reliability and usability of such systems are highly based on their accuracy as well as cost and ease of deployment. Although the Global Positioning System (GPS) is an accurate solution for outdoor use, it can not be used indoors. A popular approach is a wireless navigation system which makes use of Received Signal Strength Indicators (RSSI). However, signal propagation, as well as surrounding noise and a dynamic environment, can affect their performance. Recent advancements in Bluetooth Low Energy (BLE) devices and the introduction of small and inexpensive beacons can alleviate the problem. In this work, we introduce an indoor navigation system with BLE beacons. To measure system accuracy an Android application was developed to collect the signal. Moreover, a Kalman filter was also developed within the application to improve the accuracy. Experimental results showed improvement of systems accuracy in three square topologies. The Kalman filter improved the accuracy up to 78.9%. while the experiments also show a correlation between the overall accuracy and how close BLE beacons are to each other.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".