Instantaneous full-motion estimation of arbitrary objects using dual Doppler radar

Dominik Kellner, M. Barjenbruch, J. Klappstein, J. Dickmann, K. Dietmayer
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引用次数: 35

Abstract

Based on high-resolution radars a new approach for determining the full 2D-motion state (yaw rate, longitudinal and lateral speed) of an extended rigid object in a single measurement is proposed. The system does not rely on any model assumptions and is independent of the exact position, expansion and orientation of the object. In comparison to related methods it is not based on temporal filtering, e.g. a Kalman Filter. These methods are subject to an initialization phase and depend heavily on compliance of the underlying dynamic model. In contrast to temporal filtering, the proposed approach reduces the time to react to critical situations that occur in many safety and advanced driving assistance applications. This paper analyzes the velocity profile (radial velocity over azimuth angles) of the object received by two Doppler radar sensors. The approach can handle white noise and systematic variations (e.g. micro-Doppler of wheels) in the signal. The proposed system is applied to predict the driving path of traffic participants. Measurement results are presented for a set-up with two 77 GHz automotive radar sensors.
利用双多普勒雷达对任意目标进行瞬时全运动估计
提出了一种基于高分辨率雷达在单次测量中确定扩展刚性物体的全二维运动状态(横摆角速度、纵向和横向速度)的新方法。该系统不依赖于任何模型假设,并且独立于物体的确切位置,扩展和方向。与相关方法相比,它不是基于时间滤波,例如卡尔曼滤波。这些方法受制于初始化阶段,并且严重依赖于底层动态模型的遵从性。与时间滤波相比,所提出的方法减少了对许多安全和高级驾驶辅助应用中出现的紧急情况做出反应的时间。本文分析了两个多普勒雷达传感器接收到的目标的速度分布(径向速度除以方位角)。该方法可以处理信号中的白噪声和系统变化(如车轮的微多普勒)。将该系统应用于交通参与者的行车路径预测。给出了两个77 GHz汽车雷达传感器的测量结果。
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