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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

Inactive Publication Date: 2018-01-19
HANGZHOU DIANZI UNIV
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, in many cases the measurement noise n k,i does not necessarily satisfy the Gaussian distribution

Method used

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  • Adaptive Estimation Method of Censored Data Parameters Based on Information Theoretic Learning
  • Adaptive Estimation Method of Censored Data Parameters Based on Information Theoretic Learning
  • Adaptive Estimation Method of Censored Data Parameters Based on Information Theoretic Learning

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Embodiment 1

[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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Abstract

The invention discloses an adaptive estimation method of censored data parameters based on information theory learning. The present invention comprises the following steps: Step 1, design the estimated error function of the censored signal; Step 2, calculate the secondary Renyi entropy of the estimated error; Step 3, adopt the gradient descent method to obtain the local estimation of each node parameter; Step 4 1. According to the local estimation of each node and its neighbor nodes, design the weighting coefficient; step 5, the node and its neighbor nodes exchange their respective local estimates, and use the weighting coefficient obtained in step 4 to calculate the weighted estimation of the node and its neighbor nodes. The present invention 1) realizes the adaptive estimation of the parameters contained in the missing signal; 2) realizes the cooperative estimation of each node of the sensor network; 3) can effectively reduce the influence of the signal abnormal value on the estimation performance caused by the network node being attacked; 4) in Gaussian or It has better estimation accuracy in non-Gaussian noise environment.

Description

technical field [0001] The invention belongs to the technical field of statistical signal processing, in particular to an adaptive estimation method of censored data parameters based on information theory learning. Background technique [0002] A sensor network is a distributed network system composed of a large number of sensor nodes, which can collaboratively monitor, perceive and collect physical information of various environments or monitoring objects in the network coverage area in real time, and process and transmit it. Signal parameter estimation is an important application of sensor network. It uses various algorithms to obtain estimates of unknown physical quantities (such as temperature, target orientation, motion speed, etc.) from the measurement values ​​of multiple sensors polluted by noise. The distributed estimation method does not require a central processing unit, and has high reliability and robustness; it reduces the data transmission and processing of th...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L25/02
Inventor 刘兆霆余旺科
Owner HANGZHOU DIANZI UNIV
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