论文标题
使用Sonar感应中的深神经网络进行目标几何估计
Target Geometry Estimation Using Deep Neural Networks in Sonar Sensing
论文作者
论文摘要
目标形状的准确成像是宽带FM生物纳尔在回声蝙蝠中的关键方面，为此，我们开发了新的算法，为计算域中复杂目标的形状提供了解决方案。我们使用复发性神经网络和卷积神经网络来确定构成目标结构的闪光数（即主要反射表面）以及闪光之间的距离（声纳目标形状）。相对于广播而言，回声是脱节的，并且在短时间段扫描了dephir的频谱图，以查找由不同的Interglint延迟分离引起的局部光谱纹波模式。通过连续的时间窗口切片，我们模仿了蝙蝠听觉系统中的时频神经处理，作为一种新颖的机器人传感实时目标歧视手段。
Accurate imaging of target shape is a crucial aspect of wideband FM biosonar in echolocating bats, for which we have developed new algorithms that provide a solution for the shape of complicated targets in the computational domain. We use recurrent neural networks and convolutional neural networks to determine the number of glints (i.e., major reflecting surfaces) making up the target's structure and the distances between the glints (target shape in sonar). Echoes are dechirped relative to broadcasts, and the dechirped spectrograms are scanned in short time segments to find local spectral ripple patterns arising from different interglint delay separations. By proceeding in successive time-window slices, we mimic time-frequency neural processing in the bat's auditory system as a novel means of real-time target discrimination for sonar sensing in robotics.
