基于约束张量分解的稀疏非均匀阵列AFDM ISAC系统目标感知
Constrained Tensor Decomposition-Based Target Sensing for Sparse Non-Uniform Array-Enabled AFDM ISAC Systems
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中文总结 AI 辅助
针对稀疏非均匀阵列AFDM ISAC系统目标参数估计问题，提出基于约束张量分解的感知框架，开发流形约束交替最小二乘算法及迭代一维黄金分割搜索来估计参数，仿真表明算法接近CRB且优于传统方法。
中文摘要 AI 辅助
稀疏非均匀阵列支持的仿射频分复用（AFDM）是集成感知与通信（ISAC）的一个有前景的候选方案，但其性能关键取决于准确的目标参数估计。本文提出了一种基于约束张量分解的用于延迟、多普勒和角度估计的感知框架。具体而言，通过利用稀疏阵列几何结构开发了一种流形约束交替最小二乘（ALS）算法，实现稳健因子矩阵提取和直接角度估计。从分解的因子矩阵中，进一步应用迭代一维黄金分割搜索来细化延迟和多普勒频移。仿真结果表明，该算法接近克拉美罗界（CRB）且显著优于无约束ALS和传统方法，验证了其对稀疏非均匀阵列支持的AFDM ISAC系统的有效性。
英文摘要
Sparse non-uniform array-enabled affine frequency division multiplexing (AFDM) is a promising candidate for integrated sensing and communication (ISAC), while its performance critically depends on accurate target parameter estimation. In this paper, we propose a constrained tensor decomposition-based sensing framework for delay, Doppler, and angle estimation. Specifically, a manifold-constrained alternating least squares (ALS) algorithm is developed by exploiting the sparse array geometry structure, enabling robust factor matrix extraction and direct angle estimation. From the decomposed factor matrices, we further apply an iterative one dimensional golden section search to refine delay and Doppler shift. Simulation results demonstrate that the proposed algorithm nearly attains Cramér-Rao bound (CRB) and significantly outperforms unconstrained ALS and conventional methods, validating its effectiveness for sparse non-uniform array-enabled AFDM ISAC systems.
