A Fast Estimation Method of Signal Sparsity in Signal Reconstruction
A technology for signal sparseness and signal reconstruction, which is applied in digital transmission systems, electrical components, and error prevention. It can solve the problems of unsatisfactory accuracy and reliability of signal sparsity, and the time-consuming trial process, so as to avoid signal sparsity. The estimated value of the degree deviates too much from the true value, reduces the signal sampling rate, and relieves the pressure of data transmission and storage
Inactive Publication Date: 2017-02-15
NINGBO UNIV
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[0003] The existing signal sparsity estimation methods gradually determine the signal sparsity by trial and error. This kind of method not only takes a long time to test the process, but also stops the trial process when the test sparsity is between the upper and lower bounds of the signal sparsity. , the accuracy and reliability of the tentatively obtained signal sparsity are not ideal
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[0015] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.
[0016] A method for fast estimation of signal sparsity in signal reconstruction proposed by the present invention, its overall realization block diagram is as follows figure 1 As shown, it includes the following steps:
[0017] ① At the sending end of the communication system, according to the random sampling principle in the existing compressed sensing theory, the M×N-dimensional observation matrix A is used to randomly sample the N×1-dimensional transmitted signal vector X, and the M×1-dimensional The observation vector, denoted as Y, Y=AX, and then the observation vector Y is sent to the receiving end of the communication system, where M represents the number of sampling points randomly sampled, N represents the length of the transmitted signal vector X, 00, the observation matrix A satisfies the following conditions: δ is to satisfy the ...
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The invention discloses a fast estimation method for sparseness of signals during signal reconstruction. At the transmitting end of a communication system, according to the random sampling principle in the compressive sensing theory, random compressed sampling is carried out on vectors of transmitted signals through a measurement matrix, so that the signal sampling rate is greatly reduced, and the pressure of data transmission and storage is relieved. At the receiving end of the communication system, the upper bound and the lower bound of the sparseness of the signals are directly determined according to the restricted isometry property, and an estimation value of the sparseness of the signals is determined according to the upper bound and the lower bound of the sparseness of the signals. Compared with existing estimation methods, the time for estimation is saved, and the efficiency of estimating the sparseness of the signals is improved. Meanwhile, a mid value between the upper bound and the lower bound of the sparseness of the signals serves as the estimation value of the sparseness of the signals, and therefore the estimation value of the sparseness of the signals can be effectively prevented from excessively deviating from a true value, and the reliability of the estimation value of the sparseness of the signals is improved.
Description
technical field [0001] The invention relates to a signal reconstruction technology in a communication system, in particular to a method for quickly estimating signal sparsity in signal reconstruction. Background technique [0002] According to the Nyquist sampling theorem, only when the sampling rate is more than twice the signal bandwidth, can the analog signal be recovered without distortion from the discrete signal obtained by sampling. However, with the rapid development of science and technology, the demand for information is increasing day by day, the signal bandwidth is getting wider and wider, and the requirements for sampling rate and processing speed in information acquisition are also getting higher and higher. The compressed sensing theory proposed in recent years can randomly sample the original signal far below the Nyquist standard, and accurately reconstruct the original signal; the compressed sensing theory can realize the sampling and compression of the sign...
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IPC IPC(8): H03M7/30H04L1/00H04L25/02
Inventor 李有明刘小青季彪李程程雷鹏郭涛
Owner NINGBO UNIV