Multitarget track before detect with MIMO radars
Bibliographic record
Abstract
Recent advances in Multiple-Input-Multiple-Output (MIMO) radar systems show that they have the potential to improve detection and localization performance of targets over bistatic and multistatic radars. Unlike beam forming, which presumes a high correlation between signals either transmitted or received by an array, the MIMO system exploits the independence between signals at the array elements due to transmit diversity. Previous works focus on waveform design, signal processing and target localization with MIMO radars while no attention has been given to tracking algorithms. In this work, the problem of tracking multiple targets using MIMO radars is considered. The scenario includes multiple targets in a widely-separated MIMO architecture in which Radar-Cross-Section (RCS) diversity can be utilized. Multi target version of Track-Before-Detect (TBD) algorithm is implemented for the collected M × N orthogonal signals at the receiver, where M is the number of transmitters and N is the number of receivers. Besides having the advantage of integrating information over time on unthresholded measurements to yield detection and tracking simultaneously, the TBD technique enables tracking and detecting targets in low Signal-to-Noise-Ratio (SNR) environments. Also, a modified multiple sensor TBD, which weights the target observability to the sensor as a result of target RCS diversity in the likelihood calculation to best fit the centralized MIMO tracking is proposed. Finally, Monte Carlo simulations are performed to evaluate the performance of the proposed tracking algorithm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".