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

机译:新的马尔可夫链蒙特卡洛方法估计多个线性调频信号的最大似然参数

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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.
机译:本文提出了一种新的估计加性高斯白噪声中多个线性调频信号参数的方法。该方法将全局优化定理与新的马尔可夫链蒙特卡罗算法相结合,称为模拟退火一次可变一次随机游动Metropolis-Hastings算法。这是最大似然估计的计算适度实现,并且没有错误传播影响。仿真结果表明,即使各个线性调频信号的参数间隔很近,即使在低信噪比的情况下,该方法也能很好地估计未知参数。

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