基于非平稳马尔可夫随机场的散射环境联合感知与信道估计
Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field
AI总结：
针对通感一体化中目标兼作散射体的二维联合突发稀疏信道，提出非平稳MRF与EM联合感知估计框架，并用无求逆VB和伪似然近似降低复杂度，性能更优且开销显著下降。
摘要：
本文考虑一种通感一体化系统，其中部分雷达目标同时充当通信散射体。基于散射环境中目标与散射体的位置，提出了一种位置域信道建模方法，由此得到的雷达信道和通信信道呈现二维（2-D）联合突发稀疏性。我们提出一种散射环境联合感知与信道估计方案，以同时提升目标/散射体定位和信道估计性能，其中提出空间非平稳马尔可夫随机场（MRF）模型来刻画二维联合突发稀疏性。设计了一种基于期望最大化（EM）的方法来求解该联合估计问题，其中E步获得雷达和通信信道的贝叶斯估计，M步自动学习MRF中的动态位置网格和先验参数。然而，E步中使用的现有稀疏贝叶斯推理方法在每次迭代时都涉及高复杂度的矩阵求逆。此外，由于复杂的非平稳MRF先验，M步的复杂度呈指数级增长。为解决这些困难，我们为E步提出一种无求逆变分贝叶斯推理算法，并为M步提出一种基于伪似然近似的低复杂度方法。仿真结果表明，所提方案能够取得优于现有最先进方法的性能，同时显著降低计算开销。
英文摘要：
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.
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