An Expectation Maximization Based Simultaneous Registration and Fusion Algorithm for Radar Networks
Bibliographic record
Abstract
In this paper, we present an expectation maximization (EM) based simultaneous registration and fusion algorithm for multiple radars network. This simultaneous registration and fusion approach has advantages over other track registration techniques such as augmented Kalman filtering approach. Systematic biases (including radar time bias, radar two angular biases, and radar range bias) are estimated using the EM algorithm. The EM algorithm can guarantee that the estimated systematic biases can converge to a stationary point of the maximum likelihood function. In order to track maneuvering targets, three kinematic models are used to have a more complete description of the motion of a target: the constant velocity (CV), the constant acceleration (CA), and the coordinated turn (CT) models. The conditional expectations and covariances of the system states can be effectively computed by interactive multiple model (IMM) approach. The IMM method is a recursive hybrid filtering technique that provides a good balance between performance and complexity. We employ a fixed-interval smoother-based IMM approach to estimate the system states. The forward and backward Kalman filters are used to obtain a smooth estimate. The IMM approach is combined with the EM algorithm for simultaneous registration and fusion. The proposed algorithm consists of two steps: the first step is to adopt the fixed-interval smoother-based IMM method to calculate the conditional expectation of the log likelihood function using the current estimate of the systematic biases and the observations; the second step updates the parameter estimate by maximizing the log likelihood function
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".