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.

中文翻译：

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使用联合距离多普勒特征的多传感器空间关联


我们研究使用来自雷达传感器网络的一组测量值来定位多个目标的问题。这种“单快照成像”提供了及时的态势感知，但既不能像合成孔径雷达那样利用平台运动，也不能像卡尔曼滤波及其变体那样跨时间跟踪目标。将测量与目标相关联成为此设置中的基本瓶颈。在本文中，我们提出了一种计算高效的方法，通过识别和利用固有的几何特征，使用 FMCW 雷达传感器线性阵列来提取多个目标的 2D 位置和速度，从而大大降低空间关联的复杂性。所提出的框架对于检测异常具有鲁棒性，并且与传统方法相比，复杂度降低了几个数量级。虽然我们的方法与传统的基于 FFT 的距离多普勒处理兼容，但我们表明，更复杂的距离多普勒估计技术可以降低数据关联的复杂性，并提高目标位置和速度的估计精度。