Current spaceborne precipitation radars, including the Global Precipitation Measurement Dual-frequency Precipitation Radar and the FengYun-3G Precipitation Measurement Radar (FY-3G PMR), provide unique three-dimensional (3D) observations of global precipitation systems. However, limited spatiotemporal sampling and relatively long revisit intervals constrain the monitoring of rapidly evolving storms. This study proposes a discrete multi-beam precipitation radar concept for small-satellite constellations and develops a radar-constrained active–passive inpainting framework for reconstructing unsampled inter-beam observations. Collocated FY-3G PMR reflectivity and Microwave Radiation Imager for the Rainfall Mission (MWRI-RM) brightness temperatures observed from the same platform are used to emulate sparse radar sampling. The proposed model integrates sparse radar reflectivity and 26 passive microwave channels within a squeeze-and-excitation U-Net. Independent experiments are conducted for the PMR Ku- and Ka-band observations under different beam-spacing configurations. Reconstruction performance is evaluated exclusively within the masked inter-beam regions, and a boundary-smoothness constraint promotes continuity between reconstructed and observed beams. Channel-ablation experiments are further used to diagnose model sensitivity to individual passive microwave channels. The results demonstrate that sparse radar profiles and passive microwave observations can jointly recover coherent 3D reflectivity structures over a range of sampling densities. Across both radar frequencies, the overall mean absolute error and error standard deviation remained below approximately 2.0 and 3.0 dB, respectively, across configurations in which one to five beams were skipped between adjacent retained beams. The dependence of reconstruction accuracy on beam spacing provides quantitative guidance for balancing beam number, cross-track sampling extent, and constellation cost, while the channel-sensitivity results inform the design of future active–passive payloads. The proposed framework also provides a transferable basis for retrieval and reconstruction algorithms for future sparse-sampling radar missions.