非线性数字后处理以减轻采样中的抖动
Nonlinear Digital Post-Processing to Mitigate Jitter in Sampling
摘要：
本文介绍了几种新的算法估计的周期性带限信号的参数，从样本损坏的抖动（定时噪声）和加性噪声。经典的（非随机）和贝叶斯公式被认为是：期望最大化（EM）算法的开发计算的极大似然（ML）估计的经典估计框架，和两个吉布斯采样器提出了近似的贝叶斯最小二乘（BLS）估计独立分布的参数根据一个统一的先验。仿真结果表明，使用这些算法相比，线性估计实现显着的性能改善。最大似然估计也比较克拉美-罗下限，以确定抖动的范围，估计近似有效。这些仿真证明，本文推导的非线性算法可以容忍比线性估计器高1.4-2倍的抖动，从而将片内ADC功耗降低50- 75%。
英文摘要：
This paper describes several new algorithms for estimating the parameters of a periodic bandlimited signal from samples corrupted by jitter (timing noise) and additive noise. Both classical (non-random) and Bayesian formulations are considered: an Expectation-Maximization (EM) algorithm is developed to compute the maximum likelihood (ML) estimator for the classical estimation framework, and two Gibbs samplers are proposed to approximate the Bayes least squares (BLS) estimate for parameters independently distributed according to a uniform prior. Simulations are performed to demonstrate the significant performance improvement achievable using these algorithms as compared to linear estimators. The ML estimator is also compared to the Cramer-Rao lower bound to determine the range of jitter for which the estimator is approximately efficient. These simulations provide evidence that the nonlinear algorithms derived here can tolerate 1.4-2 times more jitter than linear estimators, reducing on-chip ADC power consumption by 50-75 percent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
