k-Space Decomposition-Based 3-D Imaging With Range Points Migration for Millimeter-Wave Radar

In this article, we present a novel method that incorporates the range points migration (RPM) method, k-space decomposition-based accurate, and noise-robust range extraction filter for microwave or millimeter-wave (MMW) short-range radar using a considerably lower fractional bandwidth signal. The advantage for higher angular resolution in higher frequency systems, such as MMW radar, has been implemented to the incoherent-based RPM method, using the simple 1-D or 2-D Fourier transform-based processing to maintain the imaging accuracy in RPM processing for both the range and the angular directions. As an additional advantage of our method, it also offers data clustering in k-space, which can enhance the imaging accuracy of the RPM method. The numerical and experimental tests demonstrated that the proposed method offers numerous advantages over the Capon-based super-resolution algorithm or coherent-based imaging approaches.

Automated summary

This article presents a novel radar imaging method combining RPM and k-space decomposition, achieving higher angular resolution with lower fractional bandwidth signals and improved imaging accuracy through data clustering in k-space. Numerical and experimental tests show the method outperforms Capon-based super-resolution algorithms and coherent-based imaging approaches.