A Kalman filtering-based method for signal reconstruction in complex environments

A Kalman filter and complex environment technology, applied in the field of signal processing, can solve problems such as low efficiency and high computational complexity

Active Publication Date: 2016-03-02
ANHUI UNIVERSITY
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Problems solved by technology

[0004] The purpose of the present invention is to solve the defects of high computational complexity and low efficiency in the...

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  • A Kalman filtering-based method for signal reconstruction in complex environments
  • A Kalman filtering-based method for signal reconstruction in complex environments
  • A Kalman filtering-based method for signal reconstruction in complex environments

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Embodiment Construction

[0061] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:

[0062] In the daily complex environment, the signal measurement value is seriously polluted by noise, such as the noise is Gaussian noise, obeying the mean value is zero, and the variance is The Kalman filter-based signal reconstruction method in a complex environment described in the present invention can further simplify the coding process of compressed sensing and improve the reconstruction accuracy, such as figure 1 As shown, it includes the following steps:

[0063] The first step is to quickly compress the signal. Design sparse measurement matrix Φ M×N , to obtain the measurement value y by performing compression measurement in a complex environment. First of all, it is necessary to design a suitable compressed measurement m...

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Abstract

The invention relates to a Kalman filtering-based method for signal reconstruction in complex environments and solves the problems of high computation complexity and low efficiency in the prior art. The method comprises the steps of performing quick compression on signals, designing a sparse measurement matrix and obtaining measurement values through compression measurement in a complex environment; establishing a prior model of the signals, inputting the sparse rate of a signal and establishing the prior model of the signal; performing a belief propagation calculation on a bipartite graph; obtaining initial values of signal estimation by using approximate MMSE estimation; obtaining signal estimation values by using Kalman filtering. The simple sparse measurement matrix is used and the storage of the measurement matrix is simplified; during signal reconstruction, the bipartite graph and the Kalman filtering-based signal estimation method are used, so that a coding processing of compressive sensing is simplified and the reconstruction precision is improved.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a Kalman filter-based signal reconstruction method in a complex environment. Background technique [0002] Compressed sensing is a new signal acquisition technology that can obtain distortion-free reconstruction of signals at a low sampling rate lower than Shannon's sampling theorem. For some practical application environments, such as ultra-wideband communication, medical imaging, wireless sensor network systems, and radar applications, on the one hand, large signal bandwidth will lead to high-speed sampling and generate massive data, resulting in huge pressure on storage and communication; On the one hand, due to the complexity of the application environment, the collected signal contains a lot of noise, which makes it difficult to recover the signal. Compressed sensing technology provides a good idea to solve the above problems, that is, if the signal is sparse on a...

Claims

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Application Information

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IPC IPC(8): H03M7/30
Inventor 蒋芳胡艳军
Owner ANHUI UNIVERSITY
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