<title>EM-ML algorithm for track initialization with features using possibly noninformative data</title>
Bibliographic record
Abstract
Initializing and maintaining a track for a low observable (low SNR, low target detection probability and high false alarm rate) target can be very challenging because of the low information content of measurements. In addition, in some scenarios, target-originated measurements might not be present in many consecutive scans because of mispointing, target maneuvers or erroneous preprocessing. That is, one might have a set of noninformative scans that could result in poor track initialization and maintenance. In this paper an algorithm based on the Expectation-Maximization (EM) algorithm combined with Maximum Likelihood (ML) estimation is presented for tracking slowly maneuvering targets in heavy clutter and possibly non-informative scans. The adaptive sliding-window EM-ML approach, which operates in batch mode, tries to reject or weight down non-informative scans using the Q-function in the M-step of the EM algorithm. A track validation technique is used to confirm the validity of the EM-ML estimates. It is shown that target features in the form of, for example, amplitude information, can also be used to improve the estimates. In addition, performance bounds based on the supplemented EM (SEM) technique are also presented. The effectiveness of new algorithm is first demonstrated on a 78-frame Long Wave Infrared (LWIR) data sequence consisting of an F1 Mirage fighter jet in heavy clutter. Previously, this scenario has been used as a benchmark for evaluating the performance of other track initialization algorithms. The new EM-ML estimator confirms the track by frame 20 while the ML-PDA (Maximum Likelihood estimator combined with Probabilistic Data Association) algorithm, the IMM-MHT (Interacting Multiple Model estimator combined with Multiple Hypothesis Tracking) and the IMM-PDA estimator previously required 28, 38 and 39 frames, respectively. The benefits of the new algorithm in terms of accuracy, early detection and computational load are illustrated using simulated scenarios as well.
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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 | 0.001 |
| 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 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".