Indoor Object Localization and Tracking Using Deep Learning over Received Signal Strength
Bibliographic record
Abstract
This paper introduces a new indoor-localization approach using a deep-learning network for which the received signal-strength indicator (RSSI) is adopted as the radiofrequency fingerprint. In our proposed scheme, the RSSIs which are estimated by a channel-propagation emulator software, are adopted as the input features for deep learning. This approach is measurement-free and thus it is very cost-effective and convenient to users. A multilayer perceptron (MLP) is constructed to predict the location(s) of the mobile object(s). The time evolution of the predicted locations of an object will form the predicted trajectory thereby. Because deep-learning networks require tremendous training data to achieve good prediction accuracy, we propose to partition the indoor geometry of interest (ex., a room) into several zones. Preliminary simulation results demonstrate that the AUC (area under the receiver-operating characteristic curve) can reach up to 0.89 for a room partitioned into eight zones. Our proposed new indoor-localization scheme in this work can be a rare but promising localization technology, which is neither passive nor active as other existing prevalent localization methods.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".