Singular-Value-QR Decomposition (SVD-QR) is a new data reduction algorithm, which can be applied to compress high data redundancy in wireless communication system and radar system. It selects the most important part of the original data which can represent other information as well. Based on the analysis of synthetic aperture radar (SAR) history data in slow-time domain, we prove that it satisfies the condition of SVD-QR approach. In addition, backprojection image reconstruction algorithm is also presented in this work, which is more efficient than matched filter method. Simulations are performed according to the Gotcha Volumetric SAR Data Set, which is collected in a real 2D/3D scenario. From the simulation results, the effectiveness of this new algorithm is verified and the compression ratio can be achieved by 2:5. Comparing with the uniform down sampling method, SVD-QR algorithm can save about 60 % data and have a better performance.
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