论文标题
使用关节范围多普勒特征的多传感器空间关联
Multi-sensor Spatial Association using Joint Range-Doppler Features
论文作者
论文摘要
我们使用一组来自雷达传感器网络的测量值研究了多个目标的问题。这样的“单快照成像”提供了及时的情境意识，但是可以像合成的孔径雷达一样使用平台运动，也不能像跨时间的轨道目标一样，如卡尔曼滤波及其变体所示。在这种情况下，将测量与目标关联成为一种基本瓶颈。在本文中，我们提出了一种计算有效的方法，可以通过识别和利用固有的几何特征来大大降低空间关联的复杂性，从而使用FMCW雷达传感器的线性阵列提取多个目标的2D位置和速度。所提出的框架对于检测异常是可靠的，与常规方法相比，复杂性降低了。尽管我们的方法与常规的基于FFT的范围多普勒处理兼容，但我们表明，用于范围多普勒估计的更复杂的技术导致数据关联复杂性降低以及目标位置和速度的更高准确性估计值。
We investigate the problem of localizing multiple targets using a single set of measurements from a network of radar sensors. Such "single snapshot imaging" provides timely situational awareness, but can utilize neither platform motion, as in synthetic aperture radar, nor track targets across time, as in Kalman filtering and its variants. Associating measurements with targets becomes a fundamental bottleneck in this setting. In this paper, we present a computationally efficient method to extract 2D position and velocity of multiple targets using a linear array of FMCW radar sensors by identifying and exploiting inherent geometric features to drastically reduce the complexity of spatial association. The proposed framework is robust to detection anomalies, and achieves order of magnitude lower complexity compared to conventional methods. While our approach is compatible with conventional FFT-based range-Doppler processing, we show that more sophisticated techniques for range-Doppler estimation lead to reduced data association complexity as well as higher accuracy estimates of target positions and velocities.
