Indoor Positioning System Using a Single-Chip Millimeter Wave Radar

Estimation of indoor location is a key capability for indoor location-based services. Among various solutions, single-chip low-cost millimeter-wave (mmWave) radar as an emerging technology are getting more and more attention due to their inherent advantages such as penetration ability, low-power consumption. Currently, the vast majority of work is focused on hybrid positioning based on mmWave radar with other sensors, and there is a lack of research on single radar indoor positioning. In this article, we propose a novel indoor positioning system based on single-radar sensor, which determines the trajectory by estimating the velocity and heading of moving radar from sparse point clouds. Doppler velocity of detected object can be able to feedback the radar velocity, and for another hand, the proposed Cluster Center Association (CCA) method combined with normal distributions transform (NDT) can be used for heading estimation. In addition, we proposed the motion consistency constraints to enhance the robustness of the system. The experimental results show that the traditional NDT algorithm will fail due to the sparse point cloud and severe indoor multipath, and the proposed algorithm can provide the positioning accuracy within 1 m and the heading estimation error of 3 degrees

Automated summary

Indoor location estimation plays a crucial role in indoor location-based services. Single-chip low-cost millimeter-wave (mmWave) radar is gaining increasing attention as an emerging technology due to its advantages such as penetration ability and low-power consumption. However, most research focuses on hybrid positioning combining mmWave radar with other sensors, and there is a lack of research on single radar indoor positioning. In this article, a novel indoor positioning system based on a single-radar sensor is proposed, which estimates the trajectory using velocity and heading estimation from sparse point clouds. The proposed algorithm shows promising results with positioning accuracy within 1 meter and heading estimation error of 3 degrees.