A constraint sufficient statistics based distributed particle filter for bearing only tracking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A constrained sufficient statistic based distributed implementation of the particle filter (CSS/DPF) is proposed for angle/bearing-only tracking (BOT) applications. The CSS/DPF runs localized particle filters at each sensor node and computes the global sufficient statistics (GSS) of the overall system as a function (summation) of the local sufficient statistics (LSS). The CSS/DPF is, therefore, a two stage procedure: (i) First, the means of LSS at local nodes are computed by running average consensus algorithms to derive the GSS, and; (ii) Each node then updates its localized particle filter using the modified GSS. Simulation results show that the CSS/DPF is near-optimal with its performance almost identical to that of the centralized particle filter. The number of average consensus runs in the CSS/DPF are reduced by an order of magnitude of the dimension of the state vector, thereby, reducing the communication complexity and bandwidth requirement of the distributed implementation.
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.
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 | 0.001 | 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 | 0.001 | 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 it