Abstract:International audienceIn this work, a Compressed Sensing (CS) strategy is developed in order to jointly achieve two complementary tasks regarding sound sources: localization and identification. Here, the sources are assumed sparse in the spatial domain, and greedy techniques are used for their localization. The case of coherent sources located in a plane is studied both numerically and experimentally at different frequencies. Results show that, in this framework, CS source localization is reliable using a sign… Show more
“…• generalizations of the MUSIC (MUltiple SIgnal Classification) algorithm [3,4] EM is now in IRIT (INPT, UPS, UT2J, UT1, CNRS), 118 route de Narbonne F-31062 Toulouse, CEDEX 9 • or group-sparsity models and corresponding identification methods (mixed-norms, block matching pursuits, etc.) [5].…”
In the most general case, source localization has to take into account the radiation pattern of the sources of interest. This is particularly important when the sensors surround the sources, and the sources are anisotropic, as is the case in several applications (EEG, speech, musical instruments, etc.). Cramér-Rao bounds for the joint estimation of the position of a source and its radiation pattern are computed for simple cases of sensor array geometries and source models, showing that a good match between the source and the model improves the Cramér-Rao bounds. It is also shown that, in general, using a model more complex than the source makes the Fisher information matrix singular. These results are supported by numerical simulations and physical interpretations.
“…• generalizations of the MUSIC (MUltiple SIgnal Classification) algorithm [3,4] EM is now in IRIT (INPT, UPS, UT2J, UT1, CNRS), 118 route de Narbonne F-31062 Toulouse, CEDEX 9 • or group-sparsity models and corresponding identification methods (mixed-norms, block matching pursuits, etc.) [5].…”
In the most general case, source localization has to take into account the radiation pattern of the sources of interest. This is particularly important when the sensors surround the sources, and the sources are anisotropic, as is the case in several applications (EEG, speech, musical instruments, etc.). Cramér-Rao bounds for the joint estimation of the position of a source and its radiation pattern are computed for simple cases of sensor array geometries and source models, showing that a good match between the source and the model improves the Cramér-Rao bounds. It is also shown that, in general, using a model more complex than the source makes the Fisher information matrix singular. These results are supported by numerical simulations and physical interpretations.
We introduce a generalization of the MUSIC algorithm to treat block-sparse signals in a multi-measurement vector framework. We show, through theoretical analysis and numerical experiments, that the requirements in terms of number of snapshots and number of measurements depend not only on the sparsity and on the size of the blocks, but also on the rank of the matrices of coefficients for each block. We apply this algorithm to the localization of directive sources, which can be modeled by block-sparsity in a dictionary of multipoles, and show that it compares favorably to a greedy approach based on the same model.
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