Localization of multiple acoustic sources in a non-ideal environment has a number of difficulties, among which are accurate acoustic feature estimation for multiple sources and association uncertainty between measurements and their corresponding sources. This paper focuses more on the latter and proposes an algorithm based on a multiple-hypothesis framework for both a measurement model and a measurement association model to localize multiple sources. A conditional data likelihood model based on a measurement hypothesis is proposed and implemented using particles. Simulation results demonstrate that the proposed algorithm is capable of localizing the positions of multiple sources with a small number of microphones without any prior knowledge when the amount of reverberation is moderate.
The photoinduced reflectivity change of optimally doped BaFe1.87Co0.13As2 is investigated. It is observed that the nematic signal in the photoinduced reflectivity change continues to increase as temperature decreases, reflecting its fluctuating nature. However, fluence‐dependent measurements show saturation of the nematic signal at higher pump fluence, a typical behavior of a static order parameter. Temperature‐dependent evolution of the saturation and the characteristic relaxation time of the nematic signal suggest that the nematicity of a spin and/or orbital origin develops at lower temperature on top of the nematicity coupled to the lattice that dominates the higher temperature nematic response.
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