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- W2118959433 abstract "This paper presents a new Kalman filter-based power spectral density estimation (PSD) algorithm for nonstationary pressure signals. The pressure signal is assumed to be an autoregressive (AR) process, and a stochastically perturbed difference equation constraint model is used to describe the dynamics of the AR coefficients. The proposed Kalman filter frame uses variable number of measurements to estimate the time-varying AR coefficients and yield the PSD estimation with better time-frequency resolution. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for nonstationary pressure signals" @default.
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- W2118959433 date "2006-09-22" @default.
- W2118959433 modified "2023-09-29" @default.
- W2118959433 title "A New Kalman Filter-based Power Spectral Density Estimation for Nonstationary Pressure Signals" @default.
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- W2118959433 doi "https://doi.org/10.1109/iscas.2006.1692911" @default.
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