Critical acoustical systems operating in complex environments contaminated with disturbances and noise offer an extreme challenge when excited by out-of-the-ordinary, impulsive, transient events that can be undetected and seriously affect their overall performance. Transient impulse excitations must be detected, extracted, and evaluated to determine any potential system damage that could have been imposed; therefore, the problem of recovering the excitation in an uncertain measurement environment becomes one of multichannel deconvolution. Recovering a transient and its initial energy has not been solved satisfactorily, especially when the measurement has been truncated and only a small segment of response data is available. The development of multichannel deconvolution techniques for both complete and incomplete excitation data is discussed, employing a model-based approach based on the state-space representation of an identified acoustical system coupled to a forward modeling solution and a Kalman-type processor for enhancement and extraction. Synthesized data are utilized to assess the feasibility of the various approaches, demonstrating that reasonable performance can be achieved even in noisy environments.

中文翻译：

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声学中的瞬态恢复问题：基于多通道模型的反卷积方法

在由干扰和噪声污染的复杂环境中运行的关键声学系统在被异常的，冲动的，瞬态事件激发时会面临极大的挑战，这些异常事件可能未被发现并严重影响其整体性能。必须检测，提取和评估瞬态脉冲激励，以确定可能施加的任何潜在系统损坏；因此，在不确定的测量环境中恢复激励的问题成为多通道去卷积之一。恢复瞬态及其初始能量的方法尚未令人满意，特别是当测量被截断并且只有一小部分响应数据可用时。讨论了针对完全和不完全激励数据的多通道反卷积技术的发展，使用基于模型的方法，该方法基于已识别声学系统的状态空间表示，该状态系统耦合到前向建模解决方案和用于增强和提取的卡尔曼型处理器。综合数据用于评估各种方法的可行性，证明即使在嘈杂的环境中也可以实现合理的性能。