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

TL;DR

This paper introduces a joint sensing and channel estimation method using a non-stationary Markov random field model to improve localization and channel accuracy in integrated radar-communication systems, with reduced computational complexity.

Contribution

It proposes a novel joint environment sensing and channel estimation scheme with an inverse-free variational Bayesian inference and low-complexity MRF parameter learning.

Findings

Outperforms state-of-the-art methods in accuracy.

Reduces computational complexity significantly.

Enhances target and scatterer localization.

Abstract

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a two-dimensional (2-D) joint burst sparsity. We propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously, where a spatially non-stationary Markov random field (MRF) model is proposed to capture the 2-D joint burst sparsity. An expectation maximization (EM) based method is designed to solve the joint estimation problem, where the E-step obtains the Bayesian estimation of the radar and communication channels and the M-step automatically learns the dynamic position grid and prior parameters in the MRF. However, the existing sparse Bayesian inference methods used in the E-step involve a high-complexity matrix inverse per iteration. Moreover, due to the complicated non-stationary MRF prior, the complexity of M-step is exponentially large. To address these difficulties, we propose an inverse-free variational Bayesian inference algorithm for the E-step and a low-complexity method based on pseudo-likelihood approximation for the M-step. In the simulations, the proposed scheme can achieve a better performance than the state-of-the-art method while reducing the computational overhead significantly.