简介：TheBayesianapproachisconsideredasthemostgeneralformulationofthestateestimationfordynamicsystems.However,mostoftheexistingBayesianestimatorsofstochastichybridsystemsonlyfocusontheMarkovjumpsystem,fewliteratureisrelatedtotheestimationproblemofnonlinearstochastichybridsystemswithstatedependenttransitions.Accordingtothisproblem,anewmethodologywhichrelaxesquitearestrictiveassumptionthatthemodetransitionprocessmustsatisfyMarkovpropertiesisproposed.Inthismethod,ageneralapproachispresentedtomodelthestatedependenttransitions,thestateandoutputspacesarediscretedintocellspacewhichhandlesthenonlinearitiesandcomputationallyintensiveproblemoffline.ThenmaximumaposteriorestimationisobtainedbyusingtheBayesiantheory.Theefficacyoftheestimatorisillustratedbyasimulatedexample.

简介：多重信号分类(音乐)的表演关于仔细解决到达(DOA)的spaced方向的算法强烈取决于signal-to-noise比率(SNR)和快照。以便解决这个问题，由重建的一个方法有两噪音subspace的空间光谱函数和信号subspace在这份报纸被介绍。关键想法是使用在协变性矩阵包含的完整的信息并且改变分别地由他们的修订特征值在噪音和信号subspace上驾驶向量的设计重量。与MUSIC算法作比较，它也，并且显著地不增加任何计算复杂性，它有的优点同时减少噪音并且在低SNR和小样品下面保留高分辨率的能力大小的情形。模拟和实验结果被包括表明建议算法的优异表演。