AI summaryⓘ
The authors propose a system called mmAlert that uses millimeter-wave signals to locate devices and track moving targets without active transmissions from the target. By capturing signals from at least two transmitters and analyzing angles and Doppler shifts from the echoes, mmAlert estimates both the transmitters' positions and the target's path. Because finding exact solutions is complex, the authors developed a simpler method using an extended Kalman filter to improve trajectory estimates. Tests at 60GHz show the system can locate transmitters and track targets with meter-level accuracy, which gets better when multiple target paths are used. This demonstrates the advantage of using diverse trajectories for more precise localization and tracking.
millimeter-wave (mmWave)device localizationtarget trackingline-of-sight (LoS)angles-of-arrival (AoA)bistatic Doppler frequencyextended Kalman filter (EKF)trajectory estimationuplink communicationalternating optimization
Authors
Chao Yu, Bojie Lv, Chunxi Chen, Jingwen Zhang, Rui Wang
Abstract
In this paper, a cooperative passive sensing system in millimeter-wave (mmWave) band for simultaneous device localization and target tracking, namely mmAlert, is proposed. Specifically, in uplink communication with at least two transmitters, the receiver receives the line-of-sight (LoS) signals and the scattered signals off a moving target, respectively. Based on the received signals of the sensing time intervals, when a passive target moves along one or multiple unknown trajectories, mmAlert could measure the angles-of-arrival (AoAs) and bistatic Doppler frequencies of the echoes from the sensing target, and then jointly estimate the locations of the transmitters and the trajectories of the target. Specifically, the transmitters' locations and the moving target's trajectories can be searched by minimizing the weighted mean squared error of the AoA and Doppler measurements. The optimal solution of the minimization problem is prohibitive due to the large number of variables. Hence, a low-complexity algorithm based on the alternating optimization is proposed, where the extended Kalman filter (EKF) is introduced to quickly shape the trajectories. The mmAlert is implemented in a 60GHz communication testbed. The experiment shows with the received signal spanning a single trajectory, the average localization error of the transmitters and average trajectory reconstruction error are 0.76 m and 0.29 m, respectively. The average errors are suppressed to 0.07 m and 0.2 m respectively, if the received signal spanning 50 trajectories is used. This justifies the benefit of trajectory diversity in localization and tracking.