{'fraction_non_alphanumeric': 0.02404809619238477, 'fraction_numerical': 0.0, 'mean_word_length': 5.091463414634147, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Frequency estimation from proper sets of correlations

As a complement to the periodogram, low-complexity frequency estimators are of interest. One such estimator is based on Prony's method and rely on phase information of the auto correlations. Without prior knowledge of the frequency (e.g., a given frequency interval), the frequency cannot be unambiguously estimated from a single correlation only. We introduce a new method of phase unwrapping using an arbitrary number (more than one) of correlations. From this arbitrary set of correlations, we propose a weighted average estimator. We derive the asymptotic performance and show how the correlation lags should be properly chosen. From a design aspect, there is often a restriction of using a fixed number of computations. In addition, we therefore propose a strategy to find a proper set of correlation lags subject to a given computational complexity. Finally, simulation results that lend support to the theoretical findings are included.