k- and Doppler Velocity Decomposition-Based Range Points' Migration for 3-D Localization With Millimeter Wave Radar

A $k-space and Doppler velocity decomposition based on an accurate millimeter wave (mmW) 3-D target localization method is presented, using the range points' migration (RPM) method to provide multi-information associated with point clouds. The incoherent method, known as RPM, has a number of advantages over coherent localization, including avoiding a false response due to phase uncertainty or the necessity for highly accurate phase calibration in multiple arrays. However, various concerns must be addressed to retain the benefit of high-frequency mmW radar. In order to bring some benefits of high-frequency mmW radar into RPM scheme, this study introduces the k-space and Doppler velocity decomposition schemes for multiple objects moving at different speeds into the RPM scheme, where a new weighted term is introduced. In addition, the RPM point cloud is incorporated with the incoherent Doppler velocity estimation method known as the weighted kernel density (WKD) method, which provides a multifunctional 3-D localization. The mmW radar experiment in the 79-GHz band demonstrates that our proposed method achieves accurate 3-D Doppler-associated localization, even with a small aperture array.

Automated summary

This paper presents a $k$-space and Doppler velocity decomposition method for accurate target localization in millimeter wave (mmW) radar. The method avoids issues caused by phase uncertainty or the need for highly accurate phase calibration and introduces a new weighted term to handle multiple moving targets. By combining with the weighted kernel density method, multifunctional 3D localization is achieved.