OTFS调制在雷达应用中的目标参数估计新方法

Novel Target Parameter Estimation Method for OTFS Modulation in Radar Applications

摘要:

目前基于正交时频空（orthogonal time frequency space，OTFS）的雷达目标参数估计算法以在网估计为主，但在网估计算法无法有效应对现实应用中的离网现象。同时，现有离网估计算法的目标参数估计性能受建模误差的限制，也难以确保雷达系统在复杂环境下的有效应用。针对以上问题，提出了一种基于OTFS调制的动态稀疏贝叶斯（dynamic-sparse Bayesian learning，dynamic-SBL）算法。该算法在稀疏贝叶斯学习框架下引入动态虚拟网格，通过不断更新调整网格参数降低建模误差，提升目标参数估计性能。同时利用 OTFS 雷达信道特有的稀疏性对网格参数进行选择性局部更新，以降低算法的复杂度。仿真结果表明，所提算法估计的目标归一化时延和多普勒频率的均方误差较小，目标参数的估计性能优于传统离网估计方法的。该算法在实际雷达系统中具有较大应用潜力。

Abstract:

Currently radar target parameter estimation algorithms based on orthogonal time frequency space (OTFS) primarily focus on on-grid estimation. However, these on-grid estimation algorithms fail to effectively address the off-grid phenomena encountered in practical scenarios. Additionally, the target parameter estimation performance of existing off-grid estimation algorithms is limited by modeling errors, making it difficult to ensure the effective operation of radar systems in complex environments. To address these issues, a dynamic-sparse Bayesian learning (dynamic-SBL) algorithm based on OTFS modulation was proposed. The algorithm introduced a dynamic virtual grid within the sparse Bayesian learning framework, continuously updating and adjusting grid parameters to reduce modeling errors and enhance target parameter estimation performance. Furthermore, it employed the unique sparsity characteristics of the OTFS radar channel to implement selective local updates of the grid parameter, thereby reducing the computational complexity. Simulation results demonstrate that the proposed algorithm achieves a lower mean square error in estimating normalized delay and Doppler frequency, with target parameter estimation performance superior to that of traditional off-grid estimation methods. This algorithm shows considerable potential for practical applications in radar systems.