Efficient Sensing Parameter Estimation With Direct Clutter Mitigation in Perceptive Mobile Networks

In this work, we investigate sensing parameter estimation in the presence of clutter in perceptive mobile networks (PMNs) that integrate radar sensing into mobile communications. Performing clutter suppression before sensing parameter estimation is generally desirable as the number of sensing parameters can be significantly reduced. However, existing methods require high-complexity clutter mitigation and sensing parameter estimation, where clutter is firstly identified and then removed. In this correspondence, we propose a much simpler but more effective method by incorporating a multipulse-cancellation-based clutter mitigation mechanism in formulating a sparse signal model for decoupling sensing parameter estimation. Instead of performing complex clutter identification as in existing methods, clutter mitigation is performed directly on the received signals in the proposed method, thereby avoiding channel reconstruction errors and unstable suppression performance. The periodic scanning of synchronization signal blocks (SSBs) in 5G-NR is exploited and the unitary approximate message passing (UAMP) is leveraged to exploit the common support for sensing parameter estimation in the formulated sparse signal recovery problem. Simulation results show that, compared to state-of-the-art methods, the proposed method delivers significantly better performance while with substantially reduced complexity.