Method for Bayes compressed sensing signal recovery based on self-adaptive measurement matrix
A Bayesian compression and observation matrix technology, applied in the field of information and communication, can solve the problem of low precision of compressed sensing signal recovery method
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[0086] Embodiment 1. A Bayesian compressed sensing signal recovery method based on an adaptive observation matrix,
[0087] Compressive sensing theory includes the following three steps:
[0088] 1), the N×1-dimensional unknown signal f is sparse under the linear basis Ψ(N×N), namely:
[0089] f=Ψw (2)
[0090] Among them: w is an N×1-dimensional sparse signal, that is, most of its coefficients are 0;
[0091] 2) Use the M×N dimensional observation matrix Φ′ to obtain observation values:
[0092] y=Φ′f=Φ′Ψw=Φw (1)
[0093] Among them: y is the measured value of M×1 dimension, Φ=Φ′Ψ is the perception matrix of M×N dimension;
[0094] 3) Given Φ′, Ψ, and y, choose an appropriate restoration algorithm to restore f:
[0095] f ^ = Φ ′ - 1 y
[0096] 1. Design method of observation matrix
[0097] The design of the observation matri...
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