Multi-sensor Spatial Association using Joint Range-Doppler Features

We investigate the problem of localizing multiple targets using a single set measurements from network radar sensors. Such "single snapshot imaging" provides timely situational awareness, but can utilize neither platform motion, as in synthetic aperture radar, nor track across time, Kalman filtering and its variants. Associating with becomes fundamental bottleneck this setting. In paper, we present computationally efficient method to extract 2D position velocity linear array FMCW sensors by identifying exploiting inherent geometric features drastically reduce complexity spatial association. The proposed framework is robust detection anomalies, achieves order magnitude lower compared conventional methods. While our approach compatible FFT-based range-Doppler processing, show that more sophisticated techniques for estimation lead reduced data association well higher accuracy estimates target positions velocities.