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>Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices
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Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices
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机译:通过γ稀疏随机矩阵从压缩噪声投影估计信号稀疏性
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摘要
In this paper, we propose a method for estimating the sparsity of a signal from its noisy linear projections without recovering it. The method exploits the property that linear projections acquired using a sparse sensing matrix are distributed according to a mixture distribution whose parameters depend on the signal sparsity. Due to the complexity of the exact mixture model, we introduce an approximate two-component Gaussian mixture model whose parameters can be estimated via expectation-maximization techniques. We demonstrate that the above model is accurate in the large system limit for a proper choice of the sensing matrix sparsifying parameter. Moreover, experimental results demonstrate that the method is robust under different signal-to-noise ratios and outperforms existing sparsity estimation techniques.
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