Spatial Transformation Imaging Enhanced by Successive Interference Cancellation and Likelihood-Guided Refinement

Integrated sensing and communication (ISAC) technology has attracted extensive attention in the future communication systems. To achieve more precise and detailed sensing capabilities, we propose and derive an imaging algorithm named adaptive successive-interference-cancellation (SIC)-aided spatial transformation imaging (ASSTI). This algorithm leverages channel responses in conjunction with the SIC method to facilitate multi-target imaging, employs a spherical wavefront model to enable accurate imaging in both far-field and near-field scenarios, and utilizes likelihood-guided refinement to optimize imaging performance. We assess the performance of the proposed algorithm using both simulation and measurement data, demonstrating image quality through the modulation transfer function (MTF) metric. The results indicate that the ASSTI algorithm achieves enhanced image contrast and quality compared to several imaging algorithms, including back-projection (BP), matched filtering (MF) and successive-interference-cancellation-aided spatial transformation imaging (SSTI). The proposed algorithm outperforms these methods in imaging capabilities and can be effectively applied in ISAC within next-generation communication systems.