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Neural network-based geometric references recognition applied to ultrasound echo signals

机译:基于神经网络的几何参考识别识别应用于超声回波信号

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Ultrasonic sensing systems are often used in robotics for navigation purposes, such as obstacle avoidance and distance measurements. However, a very desirable but difficult task is the detection and recognition of geometric references. This is a tough task due to the hard interference caused to the ultrasonic sensing system by the environment, such as temperature variations and air convection or wind. Furthermore, the echo signal variation due to the various detected references is non linear. The use of neural networks is strongly recommended when the process involved is non linear or has a very complex mathematical model, witch is exactly the present case. The application of neural networks to the recognition of geometric references via ultrasound obtained very good results.
机译:超声波传感系统通常用于机器人,用于导航目的,例如障碍物避免和距离测量。然而,非常理想但艰巨的任务是对几何引用的检测和识别。这是由于环境对超声波传感系统造成的硬干扰,例如温度变化和空气对流或风。此外,由于各种检测到的参考引用而导致的回波信号变化是非线性的。当涉及的过程是非线性的或具有非常复杂的数学模型时,强烈建议使用神经网络的使用,巫婆正是当前情况。神经网络在通过超声波识别几何引用的识别非常好的结果。

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