Using Antenna Array in Multipath Environment for Wireless Sensor Positioning
Bibliographic record
Abstract
The rapid growth in demand for location based service has encouraged research into the performance improvement for wireless sensor positioning systems. Most of proposed localization techniques for wireless sensor networks rely on multilateration or cooperative localization. In this paper, we propose a novel approach in the context of multiple-input multiple-output (MIMO) communication technique to determine the position of sensor nodes. MIMO communication systems use antenna array in both source and receive nodes to exploit the spatial properties of the multipath channel, thereby offering more information for sensor positioning. Based on estimated multipath signal parameters such as angle-of-arrival, angle-of-departure and delay-of-arrival through adaptive array signal processing techniques, the proposed approach try to minimize the errors occurring from the estimation of multipath signal parameters and gives an optimal estimation of the position of the neighbor sensor node by simultaneously resolving a set of nonlinear location equations. Computer simulations show that the position of sensor node can be determined using only one other sensor node. The mean-square errors are measured and compared with the Cramer-Rao Lower Bound to demonstrate the performance of the proposed method.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".