Constrained Weighted Least Square Optimization for Vehicle Position Tracking
Bibliographic record
Abstract
This paper describes an effective method for vehicle positioning estimation for range-based wireless network. The problem of locating a mobile terminal has received significant attention in the field of wireless communications. Time of arrival (TOA), received signal strength (RSS), time difference of arrival (TDOA) and angle-of-arrival (AOA) are commonly used measurements for estimating the position of the vehicles. In this paper, Constrained weighted least squares (CWLS) for vehicle position tracking approach with TDOA technique describes the optimized ranging measurement for the vehicles. Kalman filter is used for smoothing range data and mitigating the NLOS errors. In proposed algorithm positioning problem is formulated in a state-space framework and the constraints on system states are considered explicitly. The paper presents a simple recursive model by using time difference of arrival based position measurement and incorporating state equality constraints in the Kalman filter. From the process of Kalman filtering, the standard deviation of the observed range data can be calculated and then used in NLOS/LOS hypothesis testing. The proposed recursive positioning algorithm, compared with a Kalman tracking algorithm that estimates the target track directly from the TDOA measurements, will be comparatively more robust to measurement errors because it updates the technique that feeds the position corrections back to the Kalman Filter. It compensates for the measured geometrical position and decreases random error influence to the position precision. Simulation results show that the proposed tracking algorithm can improve the accuracy significantly.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".