Multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method
A compression sampling and matching pursuit technology, applied in the field of information and communication
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[0106] Specific implementation mode 1. Combination Image 6 Describe this specific implementation method, multi-observation value vector sparsity adaptive compression sampling matching tracking method, the specific process is: input observation matrix A and frame matrix V, set an appropriate residual threshold θ according to the size of the signal-to-noise ratio, and combine signals according to The approximate range of sparsity sets an appropriate stage number threshold σ.
[0107] initialization order support set Residual R=V, support set candidate set According to the approximate range of signal joint sparsity, set the appropriate number of stages stage and step size step. Computes the 2-norm of each atom (column vector) in the observation matrix. Repeat the following steps until the iteration stop condition is met:
[0108] Use the formula:
[0109] s=stage×step(1)
[0110] Compute the estimated sparsity s for each iteration. Multiply the residual matrix R (initia...
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