Blind reconstruction method under modulation broadband converter based on sparse Bayesian
A sparse Bayesian, modulated broadband technology, applied in the field of signal blind reconstruction under the framework of modulated broadband converters, can solve problems such as poor reconstruction performance, and achieve the effect of improving reconstruction performance
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specific Embodiment approach 1
[0022] Specific implementation mode one: the blind signal reconstruction method under the framework of the sparse Bayesian modulation broadband converter described in this implementation mode, the method includes the following steps:
[0023] Step 1: input a sparse signal x(t), and multiply the input sparse signal x(t) with the pseudo-random sequences of m channels of the modulated broadband converter, the pseudo-random sequences of each channel are mutually orthogonal; Then multiply the result corresponding to each channel through F 0 ' sampling frequency to obtain the sampling results of each channel, and filter the sampling results of each channel through a low-pass filter with a cutoff frequency of fs / 2 to obtain the sampling value y output by each channel i (n) frequency-domain DTFT;
[0024] Step 2: Obtain the sampled value y of each channel output i (n) performing windowing processing to obtain a signal after windowing processing;
[0025] Step 3: adding Gaussian whi...
specific Embodiment approach 2
[0028] Specific implementation mode two: this implementation mode further limits the signal blind reconstruction method under the framework of the sparse Bayesian modulation broadband converter described in the first implementation mode, and the specific process of the first step is as follows:
[0029] The pseudo-random sequence for modulating the i-th channel of the wideband converter is p i (t), according to the Fourier transform, the pseudo-random sequence p i The specific expression of (t) is:
[0030] Among them: l is Fourier series, c il is the Fourier coefficient, j is the complex unit, T P is the period of the pseudo-random sequence, and t is time;
[0031] According to the inverse Fourier transform, the Fourier coefficient c is obtained il The expression is:
[0032]
[0033] Input a sparse signal x(t), multiply the input sparse signal x(t) with the pseudo-random sequence of the m channels of the modulated broadband converter, and obtain the frequency domai...
specific Embodiment approach 3
[0042] Specific implementation mode three: this implementation mode further limits the signal blind reconstruction method under the framework of the sparse Bayesian modulation broadband converter described in the first implementation mode, and the specific process of the fourth step is as follows:
[0043] The signal obtained in step 3 after adding Gaussian white noise is expressed as:
[0044] y(f)=Az(f) (5)
[0045] Wherein: intermediate variable z (f)=[z 1 (f),...,z L (f)] T , and z i′ (f)=X(f+(i'-L 0 -1) f p ), 1≤i′≤L, L=2L 0 +1; y(f) is a vector of length m, and A is the observation matrix;
[0046] After sampling and filtering, the Fourier coefficients are given by c il becomes c il ':
[0047]
[0048] Among them: a ik is the value of the pseudo-random sequence of the i-th channel, k=0,1,...,L 0 -1;
[0049] Define the integral term of formula (6) as d l :
[0050]
[0051] where: intermediate variable but
[0052] Then the observation matrix A...
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