Maximum likelihood parameter estimation of multiple chirp signals by a new Markov chain Monte Carlo approach

Y. Lin, Y. Peng, X. Wang
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引用次数: 13

Abstract

In this paper, a novel method for estimating the parameters of multiple chirp signals in additive Gaussian white noise is proposed. The method combines a global optimization theorem with a new Markov chain Monte Carlo algorithm, called the simulated annealing one-variable-at-a-time random walk Metropolis-Hastings algorithm. It is a computationally modest implementation of maximum likelihood estimation and has no error propagation effect. Simulation results show that the proposed method can give good estimates for the unknown parameters, even when the parameters of the individual chirp signals are closely spaced and the Cramer-Rao lower bound can be attained even at low signal-to-noise ratio.
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用一种新的马尔可夫链蒙特卡罗方法估计多重啁啾信号的极大似然参数
提出了一种在加性高斯白噪声中估计多重啁啾信号参数的新方法。该方法将全局优化定理与一种新的马尔可夫链蒙特卡罗算法相结合,称为模拟退火单变量随机行走Metropolis-Hastings算法。它是最大似然估计的一种计算适度的实现,并且没有误差传播效应。仿真结果表明,即使单个啁啾信号的参数间隔很近,该方法也能很好地估计未知参数,即使在低信噪比下也能达到Cramer-Rao下界。
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