A fast and accurate maximum likelihood particle filtering for the indoor DoA-based positioning
Bibliographic record
Abstract
Indoor positioning using beacons is among the most accurate solutions. For beacon-based localization to remain advantageous, their cost should be reduced without a reduction in accuracy. One possible approach to achieve this is to rely on a smaller number of beacons. For robots that navigate on planar surfaces, two unknown position parameters and one unknown heading parameter need to be estimated. Thus, a minimum of three beacons is required, assuming each beacon provides a single observation. A reduction in the minimum number of beacons can only be achieved by incorporating other sources of information, such as a motion model that provides a prediction of the robot’s location. A common approach to fuse the observation and the motion model is based on an Extended Kalman Filter (EKF). EKF-based solutions assume that the distribution of errors in the input and output variables is Gaussian. Further, they assume that the initial position of the robot is known. These assumptions are very restrictive and can lead to large errors in the solutions or failure to converge. Due to these challenges, researchers have proposed particle filtering (PF) in such scenarios. The disadvantage of PF is the significant increase in computational cost as the number of dimensions increases. In this research, we developed a new theoretical framework that can decrease this computational cost. The solution is a two-pass approach, where particles are initially sampled in 2D space, excluding one dimension for the heading. This is achieved by using a function that approximates the maximum observation likelihood with respect to the robot’s heading. Once the final pose is derived, EKF is used to smooth the trajectory. The developed particle filter solution can achieve a 12.7 cm error while relying on only two beacons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".