Adaptive Estimation Method of Censored Data Parameters Based on Information Theoretic Learning
An adaptive estimation and information theory technology, applied to baseband system components and other directions, can solve problems such as satisfying Gaussian distribution, achieve good estimation accuracy, and reduce the impact of estimation performance
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[0082] Suppose a sensor network contains K=20 nodes, and the vector w to be estimated 0 is a normalized 5×1 vector (ie ||w 0 || 2 = 1). noise n k,i It is generalized Gaussian noise, that is, the probability density of the noise satisfies f(n)∝exp(-|v| p ), where p is the shape parameter, when 0k,i is super-Gaussian noise (in particular, p=1,n k,i is super Laplacian noise), when p=2, n k,i is Gaussian noise, when p>2, n k,i is sub-Gaussian noise. Define the signal-to-noise ratio as: In addition, the width parameter adopted by the Gaussian kernel function in this method is σ=2. In the following experiments, the present method is compared with MSE-based adaptive methods:
[0083] Experiment 1: In the case of SNR=5dB, calculate the relationship between the mean square estimation error and the iterative cycle i, the results are as attached image 3 It is shown and shown that no matter in super-Gaussian, Gaussian or sub-Gaussian noise environment, the method of the presen...
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