Sensor Fusion and Optimal Platform Trajectory Planning for Ground Target Localization with Terrain Uncertainty and Measurement Biases
Bibliographic record
Abstract
A ground target can be localized using an airborne angle-only sensor. However, possible measurement bias causes a delay in error convergence. Adding measurements from a range-only sensor can improve localization by attaining faster convergence. Estimation accuracy can be improved further by optimizing the trajectories of the platforms containing the angle-only and range-only sensors. To ensure observability in 3-D localization, in most of the recent works, the height of the ground target from the sea level is assumed to be known perfectly. However, in most practical applications, target height is obtained from a Digital Terrain Elevation Database (DTED), having multiple levels of resolution. As a result, in addition to the bias uncertainty, the terrain uncertainty is required to be handled. In this work, Cramer Rao Lower Bound (CRLB) is derived for the localization problem considering the terrain and measurement bias uncertainties. A CRLB based optimization algorithm is proposed for optimal platform trajectory planning. We propose two localization approaches to handle the biases. The first approach involves bias compensation using a prior whereas the target and bias states are estimated jointly in the second approach. In both approaches, range sensor fusion is proposed to improve localization accuracy. The effectiveness of our algorithms is verified using Monte Carlo simulations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".