Passive acoustic sensing is a cost-effective solution for monitoring moving targets such as vessels and aircraft, but its performance is hindered by complex propagation effects like multi-path reflections and motion-induced artefacts. Existing filtering techniques do not properly incorporate the characteristics of the environment or account for variability in medium properties, limiting their effectiveness in separating source and reflection components. This paper proposes a method for separating target signals from their reflections in a spectrogram. Temporal filtering is applied to cepstral coefficients using an adaptive band-stop filter, which dynamically adjusts its bandwidth based on the relative intensity of the quefrency components. The method improved the signal-to-noise ratio (SNR), log-spectral distance (LSD), and Itakura-Saito (IS) distance across velocities ranging from 10 to 100 metres per second in aircraft noise with simulated motion. It also enhanced the performance of ship-type classification in underwater tasks by 2.28 and 2.62 Matthews Correlation Coefficient percentage points for the DeepShip and VTUAD v2 datasets, respectively. These results demonstrate the potential of the proposed pipeline to improve acoustic target classification and time-delay estimation in multi-path environments, with future work aimed at amplitude preservation and multi-sensor applications.

翻译：被动声学传感是监测船舶、飞机等运动目标的一种经济高效的解决方案，但其性能受到多径反射和运动诱发伪影等复杂传播效应的制约。现有滤波技术未能充分纳入环境特征或考虑介质特性的可变性，限制了其在分离源信号与反射分量方面的有效性。本文提出了一种在声谱图中将目标信号与其反射分离的方法。该方法采用自适应带阻滤波器对倒谱系数进行时域滤波，该滤波器根据倒频率分量的相对强度动态调整其带宽。在模拟运动的飞机噪声中，该方法在10至100米/秒的速度范围内改善了信噪比（SNR）、对数谱距离（LSD）和Itakura-Saito（IS）距离。在水下任务中，对于DeepShip和VTUAD v2数据集，该方法分别将船舶类型分类性能提升了2.28和2.62个马修斯相关系数百分点。这些结果表明，所提出的流程在多径环境下具有改善声学目标分类和时延估计的潜力，未来的工作将聚焦于幅度保持和多传感器应用。