Frequency shift keyed narrowband interference rejection: optimal exponential weighting factor for the RLS algorithm
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Bibliographic record
Abstract
Previous work has shown that co-channel narrowband interference can limit the performance of direct sequence spread spectrum (DSSS) and high frequency (HF) systems. Narrowband interference (NBI) can be single tone, chirped or frequency shift keyed (FSK) in nature and numerous techniques for its removal have been proposed. Linear adaptive prediction filters based on autoregressive modelling have been suggested owing to their ability to perform in a non-stationary environment. In the FSK narrowband interference case, adaptive filters are susceptible to excess residual errors owing to instantaneous frequency step changes and the finite convergence time required for the filter to adapt to a new interference frequency. The signal degradation owing to this type of interference becomes greater in high SNR regimes and has been found to be a function of the frequency parameters of the FSK interference signal. This paper discusses the convergence and frequency tracking properties of the recursive least squares (RLS) adaptive lattice filter using a posteriori estimation errors in the presence of FSK narrowband interference. An optimal exponential weighting factor that balances convergence time and steady state error is derived for this case of NBI. Results are compared to those of the previously proposed fast converging minimum frequency error (FCMFE) RLS lattice filter.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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