Title :
Methods for chaotic signal estimation
Author :
Kay, Steven ; Nagesha, Venkatesh
Author_Institution :
Rhode Island Univ., Kingston, RI, USA
fDate :
8/1/1995 12:00:00 AM
Abstract :
A dynamic programming algorithm and a suboptimal but computationally efficient method for estimation of a chaotic signal in white Gaussian noise are proposed. The nonlinear map is assumed known so that only the initial condition need be estimated. Computer simulations confirm that both approaches produce efficient estimates at high signal-to-noise ratios
Keywords :
Gaussian noise; chaos; computational complexity; dynamic programming; interference (signal); maximum likelihood detection; maximum likelihood estimation; white noise; chaotic signal estimation; dynamic programming algorithm; high signal-to-noise ratios; initial condition; nonlinear map; suboptimal but computationally efficient method; white Gaussian noise; Chaos; Convergence; Electrons; Estimation; Gaussian noise; Heuristic algorithms; Radar; Signal processing algorithms; Smoothing methods; Taylor series;
Journal_Title :
Signal Processing, IEEE Transactions on
