Constrained Tensor Decomposition-Based Target Sensing for Sparse Non-Uniform Array-Enabled AFDM ISAC Systems

Linchu Chen, Zhendong Li, Lin Chen, Zhou Su, Wenjie Wang, Wen Chen

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.