Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals

Zheng Zhao, Simo Sarkka, Jens Sjolund, Thomas B. Schon

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu


We present a continuous-time probabilistic approach for estimating the chirp signal and its instantaneous frequency function when the true forms of these functions are not accessible. Our model represents these functions by non-linearly cascaded Gaussian processes represented as non-linear stochastic differential equations. The posterior distribution of the functions is then estimated with stochastic filters and smoothers. We compute a (posterior) Cramér-Rao lower bound for the Gaussian process model, and derive a theoretical upper bound for the estimation error in the mean squared sense. The experiments show that the proposed method outperforms a number of state-of-the-art methods on a synthetic data. We also show that the method works out-of-the-box for two real-world datasets.

JulkaisuIEEE Transactions on Signal Processing
DOI - pysyväislinkit
TilaJulkaistu - 16 helmik. 2023
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu


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