Bayesian Nonparametric Tracking of Target Impulse Response for Cognitive Radars
Bibliographic record
Abstract
A characteristic feature of cognitive radars is the ability to adapt their transmitted waveforms to the impulse response of the target of interest. A typical assumption is to represent the evolution of the target impulse response (TIR) using the Gaussian linear state space (LSS) model. Based on this assumption, the Kalman filter (KF) has been used to estimate the TIR as the optimal Bayesian filter under known target and interference statistics. In practice, however, the available measured data for different targets suggest non-Gaussian TIR distributions and do not justify the assumption of an LSS generating model. In this paper, we propose a new TIR tracking method based on Bayesian nonparametric (BNP) statistics. In contrast to conventional Bayesian filters such as Kalman or particle filters, the proposed method does not require prior knowledge about the target or environmental interference statistics. This added flexibility allows us to consider non-Gaussian TIR distributions, which have not been examined in the literature heretofore. Furthermore, we propose a new TIR generating model based on the spherical invariant random process, which stands as a more realistic approach supported by published empirical data. Through extensive Monte Carlo simulations, we show that the proposed BNP method offers improved TIR tracking accuracy compared to the conventional Bayesian filters under several distributions and generating models even in harsh environments like jamming. Notably, this superior performance comes with lower complexity and without prior knowledge about the target statistics as required by the conventional Bayesian filters.
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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 | 0.009 |
| 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".