Optimal Self-Calibration for Collaborative Sensing in mmWave Radar Networks

The emergence of high-resolution millimeter-wave (mmWave) multi-input multi-output (MIMO) radar can enable a powerful framework for collaborative RF sensing with a radar network. Each node can use its range, Doppler, and angle information to track targets within its field of view (FOV), but collaborative networked sensing with multiple such nodes can provide several new capabilities for multi-target tracking, including 'cellularstyle' coverage of large areas, and robust performance under FOV limitations and line-of-sight (LoS) obstructions for individual nodes. However, collaborative target tracking and track-level fusion in a radar network requires knowledge of the radar nodes' poses (i.e., positions and orientations) relative to each other. In this paper, we propose an autocalibration strategy based on joint target tracking and pose estimation by fusing measurements corresponding to a moving target seen by multiple radars. We provide an optimal algorithm with a closed-form solution that enables any two nodes tracking a common target to determine their relative poses by matching their estimated tracks. Our preliminary results illustrate how this algorithm can be used as a building block for multi-node calibration, and target track association when tracking multiple targets.