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In this chapter, underdetermined mixing matrix estimation algorithms are studied when the source signal is not sufficiently sparse. Firstly, several mixing matrix estimation algorithms based on subspace estimation are studied. This chapter introduces the mixing matrix estimation algorithms based on k-dimension subspace, parameter estimation, plane clustering, and homogeneous polynomial representation. In addition, several underdetermined mixing matrix estimation algorithms based on single source detection are introduced, including TIFROM method, recognition time–frequency SSP method and improved recognition time–frequency SSP method.

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The Estimation of Insufficiently Sparse Underdetermined Mixing Matrix

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  • 发表日期：

    2025年

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  • Weihong Fu

摘要

In this chapter, underdetermined mixing matrix estimation algorithms are studied when the source signal is not sufficiently sparse. Firstly, several mixing matrix estimation algorithms based on subspace estimation are studied. This chapter introduces the mixing matrix estimation algorithms based on k-dimension subspace, parameter estimation, plane clustering, and homogeneous polynomial representation. In addition, several underdetermined mixing matrix estimation algorithms based on single source detection are introduced, including TIFROM method, recognition time–frequency SSP method and improved recognition time–frequency SSP method.