Abstract:

Frequency Modulated Continuous-Wave (FMCW) radar has been widely adopted in fields such as autonomous driving and security surveillance due to its high resolution, strong penetration capability, and low power consumption. However, traditional target detection methods based on two-dimensional Fast Fourier Transform (2D-FFT) suffer from high computational complexity and significant latency when processing large-scale intermediate frequency (IF) data, making it challenging to meet millisecond-level real-time requirements. To address these issues, this paper proposes a Lanczos compression-based accelerated Constant False Alarm Rate (CFAR) detection method deployed on an FPGA platform. By employing Krylov subspace projection to approximate principal component vectors, the computational complexity is reduced from O(NMlog(NM)) to O(NM). The algorithm first applies one-dimensional CFAR after principal component projection to obtain candidate points, followed by a refined two-dimensional CFAR window for target confirmation, effectively reducing computational load while maintaining detection accuracy. Experimental results on the Xilinx XC7Z020CLG400 FPGA platform demonstrate that the proposed scheme achieves a latency of only 0.36 ms when processing a 256×512 matrix, with an average power consumption of 0.76 W. In both public datasets and simulated multi-target scenarios, the system achieved an average false alarm rate of 0.48% and a miss rate of 2.93%, validating the high precision, low latency, and energy efficiency of the proposed method. This research provides a high-performance, low-power hardware solution for real-time target detection in millimeter-wave radar applications, with broad prospects for engineering applications.

Key words: frequency modulated continuous wave (FMCW), target detection, Lanczos, 2D-FFT