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This paper considers an integrated sensing and communication system, wheresome radar targets also serve as communication scatterers. A location domainchannel modeling method is proposed based on the position of targets andscatterers in the scattering environment, and the resulting radar andcommunication channels exhibit a two-dimensional (2-D) joint burst sparsity. Wepropose a joint scattering environment sensing and channel estimation scheme toenhance the target/scatterer localization and channel estimation performancesimultaneously, where a spatially non-stationary Markov random field (MRF)model is proposed to capture the 2-D joint burst sparsity. An expectationmaximization (EM) based method is designed to solve the joint estimationproblem, where the E-step obtains the Bayesian estimation of the radar andcommunication channels and the M-step automatically learns the dynamic positiongrid and prior parameters in the MRF. However, the existing sparse Bayesianinference methods used in the E-step involve a high-complexity matrix inverseper iteration. Moreover, due to the complicated non-stationary MRF prior, thecomplexity of M-step is exponentially large. To address these difficulties, wepropose an inverse-free variational Bayesian inference algorithm for the E-stepand a low-complexity method based on pseudo-likelihood approximation for theM-step. In the simulations, the proposed scheme can achieve a betterperformance than the state-of-the-art method while reducing the computationaloverhead significantly.

Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field