Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI 49931, USA.
Michigan Tech Research Institute, Ann Arbor, MI 48105, USA.
Sensors (Basel). 2018 Oct 30;18(11):3687. doi: 10.3390/s18113687.
We present an algorithm for fusing data from a constellation of RF sensors detecting cellular emanations with the output of a multi-spectral video tracker to localize and track a target with a specific cell phone. The RF sensors measure the Doppler shift caused by the moving cellular emanation and then Doppler differentials between all sensor pairs are calculated. The multi-spectral video tracker uses a Gaussian mixture model to detect foreground targets and SIFT features to track targets through the video sequence. The data is fused by associating the Doppler differential from the RF sensors with the theoretical Doppler differential computed from the multi-spectral tracker output. The absolute difference and the root-mean-square difference are computed to associate the Doppler differentials from the two sensor systems. Performance of the algorithm was evaluated using synthetically generated datasets of an urban scene with multiple moving vehicles. The presented fusion algorithm correctly associates the cellular emanation with the corresponding video target for low measurement uncertainty and in the presence of favorable motion patterns. For nearly all objects the fusion algorithm has high confidence in associating the emanation with the correct multi-spectral target from the most probable background target.
我们提出了一种算法，用于融合来自检测蜂窝发射的射频传感器星座的数据和多光谱视频跟踪器的输出，以定位和跟踪具有特定手机的目标。射频传感器测量由移动蜂窝发射引起的多普勒频移，然后计算所有传感器对之间的多普勒差分。多光谱视频跟踪器使用高斯混合模型来检测前景目标，并使用 SIFT 特征通过视频序列跟踪目标。通过将来自射频传感器的多普勒差分与从多光谱跟踪器输出计算的理论多普勒差分相关联来融合数据。计算绝对差和均方根差以关联来自两个传感器系统的多普勒差分。使用具有多个移动车辆的城市场景的合成生成数据集评估了算法的性能。在低测量不确定性和有利的运动模式下，所提出的融合算法可以正确地将蜂窝发射与相应的视频目标相关联。对于几乎所有的物体，融合算法都有很高的置信度，将发射与最可能的背景目标中的正确多光谱目标相关联。
