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Maximum Likelihood Signal Amplitude Estimation Based on Permuted Blocks of Differently Binary Quantized Observations of a Signal in Noise

机译:基于置换块的最大似然信号幅度估计   噪声信号的差分二进制量化观测

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摘要

Parameter estimation based on binary quantized observations is consideredgiven the estimation system does not know which of a set of quantizers wasused, without replacement, for each block of observations. Thus the estimationsystem receives permutated blocks of quantized samples of a signal in noisewith unknown signal amplitude. Maximum likelihood (ML) estimators are utilizedto estimate both the permutation matrix and unknown signal amplitude underarbitrary, but known, signal shape and quantizer thresholds. Sufficientconditions are provided under which an ML estimator can be found in polynomialtime. In addition, model identifiability is also studied, and an alternatingmaximization algorithm is proposed to solve the general problem via goodinitial estimates. Finally numerical simulations are performed to evaluate theperformances of the ML estimators.
机译:考虑到估计系统不知道针对每个观测块使用了一组量化器中的哪一个,而不进行了替换,因此考虑了基于二进制量化观测值的参数估计。因此,估计系统接收具有未知信号幅度的噪声中的信号的量化样本的置换块。最大似然(ML)估计器用于估计置换矩阵和任意但已知的信号形状和量化器阈值以下的未知信号幅度。提供了在多项式时间内可以找到ML估计量的充分条件。此外,还研究了模型的可识别性,并提出了一种交替最大化算法,通过良好的初始估计来解决一般问题。最后进行数值模拟以评估ML估计量的性能。

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