Abstract: ‍ ‍Recently， traditional radar imaging methods have been considerably improved. However， in forward-looking imaging scenarios， radar faces difficulties in obtaining azimuthal Doppler information， thereby limiting its azimuthal resolution. To address this issue， this study proposes a microwave correlation imaging method. The microwave correlation imaging method utilizes the principle of correlation imaging to achieve high-resolution imaging without relying on target Doppler information. This novel radar imaging method overcomes the resolution limitations imposed by radar apertures in traditional radar imaging methods and holds significant potential for forward-looking imaging. Currently， research on microwave correlation imaging， both domestic and international， primarily focuses on generating random wavefronts， solving model mismatch problems， and developing metamaterial apertures. However， the optimization of the crucial correlation process primarily focuses on compressive sensing and deep learning， with relatively limited research on pseudo-inverse algorithms. Therefore， to further improve the microwave correlation imaging system， this study proposes a novel microwave correlation forward-looking imaging method that optimizes the pseudo-inverse algorithm. This study combines truncated singular value decomposition （TSVD） processing and Tikhonov regularization to propose a joint processing method of SVD and Tikhonov regularization （TSVD-Tikhonov， TSVDT）. The TSVDT method involves processing the spatio-temporal random radiation array and then performing compressed correlation imaging. Furthermore， this paper compares generalized cross-validation （GCV） and L-curve methods and demonstrates that in microwave correlation imaging， using the GCV method for truncation parameter selection results in shorter computation time and greater stability. Finally， the proposed method was validated through microwave anechoic chamber experiments， which demonstrated its improved anti-interference capability in low signal-to-noise ratio conditions while maintaining high computational speed.