Acoustic models of railway vehicles in standstill and pass-by conditions can be used as part of a virtual certification process for new trains.For each piece of auxiliary equipment,the sound power measured on a test b...Acoustic models of railway vehicles in standstill and pass-by conditions can be used as part of a virtual certification process for new trains.For each piece of auxiliary equipment,the sound power measured on a test bench is combined with meas-ured or predicted transfer functions.It is important,however,to allow for installation effects due to shielding by fairings or the train body.In the current work,fast-running analytical models are developed to determine these installation effects.The model for roof-mounted sources takes account of diffraction at the corner of the train body or fairing,using a barrier model.For equipment mounted under the train,the acoustic propagation from the sides of the source is based on free-field Green’s functions.The bottom surfaces are assumed to radiate initially into a cavity under the train,which is modelled with a simple diffuse field approach.The sound emitted from the gaps at the side of the cavity is then assumed to propagate to the receivers according to free-field Green’s functions.Results show good agreement with a 2.5D boundary element model and with measurements.Modelling uncertainty and parametric uncertainty are evaluated.The largest variability occurs due to the height and impedance of the ground,especially for a low receiver.This leads to standard deviations of up to 4 dB at low frequencies.For the roof-mounted sources,uncertainty over the location of the corner used in the equivalent barrier model can also lead to large standard deviations.展开更多
针对时变水声信道造成的严重多途干扰问题,提出基于虚拟训练序列的双向水声信道精准估计(Virtual Training Based Bidirectional Channel Estimation,VT-BCE)算法。基于叠加训练(Superimposed Training,ST)方案,将训练序列和符号序列线...针对时变水声信道造成的严重多途干扰问题,提出基于虚拟训练序列的双向水声信道精准估计(Virtual Training Based Bidirectional Channel Estimation,VT-BCE)算法。基于叠加训练(Superimposed Training,ST)方案,将训练序列和符号序列线性叠加,使得训练序列和符号序列的信道信息一致,提高信号的跟踪能力;基于置信传播,双向信道估计(Bidirectional Channel Estimation,BCE)算法将一个数据块分成多个短块,利用整个数据块的信息估计当前短块信道,实现对当前短块的精准信道估计。将ST方案、BCE算法和信道均衡(频域)以迭代的方式相结合,使估计的符号序列可以作为信道估计的虚拟训练(Virtual Training,VT)序列,提升信道的估计性能,进而提高系统的解码性能。最后,通过计算机仿真和水池试验,验证了所提算法的有效性。展开更多
基金The work described here has been supported by the TRANSIT project(funded by EU Horizon 2020 and the Europe’s Rail Joint Undertaking under grant agreement 881771).
文摘Acoustic models of railway vehicles in standstill and pass-by conditions can be used as part of a virtual certification process for new trains.For each piece of auxiliary equipment,the sound power measured on a test bench is combined with meas-ured or predicted transfer functions.It is important,however,to allow for installation effects due to shielding by fairings or the train body.In the current work,fast-running analytical models are developed to determine these installation effects.The model for roof-mounted sources takes account of diffraction at the corner of the train body or fairing,using a barrier model.For equipment mounted under the train,the acoustic propagation from the sides of the source is based on free-field Green’s functions.The bottom surfaces are assumed to radiate initially into a cavity under the train,which is modelled with a simple diffuse field approach.The sound emitted from the gaps at the side of the cavity is then assumed to propagate to the receivers according to free-field Green’s functions.Results show good agreement with a 2.5D boundary element model and with measurements.Modelling uncertainty and parametric uncertainty are evaluated.The largest variability occurs due to the height and impedance of the ground,especially for a low receiver.This leads to standard deviations of up to 4 dB at low frequencies.For the roof-mounted sources,uncertainty over the location of the corner used in the equivalent barrier model can also lead to large standard deviations.
文摘针对时变水声信道造成的严重多途干扰问题,提出基于虚拟训练序列的双向水声信道精准估计(Virtual Training Based Bidirectional Channel Estimation,VT-BCE)算法。基于叠加训练(Superimposed Training,ST)方案,将训练序列和符号序列线性叠加,使得训练序列和符号序列的信道信息一致,提高信号的跟踪能力;基于置信传播,双向信道估计(Bidirectional Channel Estimation,BCE)算法将一个数据块分成多个短块,利用整个数据块的信息估计当前短块信道,实现对当前短块的精准信道估计。将ST方案、BCE算法和信道均衡(频域)以迭代的方式相结合,使估计的符号序列可以作为信道估计的虚拟训练(Virtual Training,VT)序列,提升信道的估计性能,进而提高系统的解码性能。最后,通过计算机仿真和水池试验,验证了所提算法的有效性。
