The Cramér–Rao Bound for Signal Parameter Estimation From Quantized Data [Lecture Notes]

Generating dithering noise for maximum likelihood estimation from quantized data

The Quantization Theorem I (QT I) implies that the likelihood function can be reconstructed from quantized sensor observations, given that appropriate dithering noise is added before quantization. We present constructive algorithms to generate such dithering noise. The application to maximum likelihood estimation (mle) is studied in particular. In short, dithering has the same role for amplitud...

Signal Amplitude Estimation and Detection from Unlabeled Binary Quantized Samples

Signal amplitude estimation and detection from unlabeled quantized binary samples are studied, assuming that the order of the time indexes is completely unknown. First, maximum likelihood (ML) estimators are utilized to estimate both the permutation matrix and unknown signal amplitude under arbitrary, but known signal shape and quantizer thresholds. Sufficient conditions are provided under whic...

Lecture Notes on an Upper Bound for the Best Cut Quotient from a Vector

• Capital letters represent matrices and bold lower-case letters represent vectors. For a matrix A, aij denotes the element in row i and column j; for the vector x, xi denotes the i th entry in the vector. • Various special matrices are represented by the following conventions: The adjacency matrix is denoted A; the degree matrix is denoted D; the Laplacian D − A is denoted L. The Laplacian can...

Lecture Notes on Bayesian Estimation and Classification

Lecture Notes -estimation and Sensor Information Fusion

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