A distributed implementation of a sequential Monte Carlo probability hypothesis density filter for sensor networks
Bibliographic record
Abstract
This paper presents a Sequential Monte Carlo (SMC) Probability Hypothesis Density (PHD) algorithm for decentralized state estimation from multiple platforms. The proposed algorithm addresses the problem of communicating and fusing track information from a team of multiple sensing platforms detecting and tracking multiple targets in the surveillance region. Each sensing platform makes multiple, noisy measurements of an underlying, time-varying state that describes the monitored system. The monitored system involves potentially nonlinear target dynamics described by Markovian state-space model, nonlinear measurements, and non-Gaussian process and measurement noises. Each sensing platform reports measurements to a node in the network, which performs sequential estimation of the current system state using the probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior of the multi-target state. A sequential Monte Carlo method is used to implement the filter. The crucial consideration is what information needs to be transmitted over the network in order to perform online estimation of the current state of the monitored system, whilst attempting to minimize communication overhead. Simulation results demonstrate the efficiency of the proposed algorithm for a team of bearing only sensors.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".