Low-Complexity Gridless Single-Snapshot DoA Estimation via Truncated Hankel Newton-MUSIC

Ruoxiao Cao, Wentao Yu, Yi Gong, Khaled B. Letaief

Reconfigurable antenna arrays can provide enhanced spatial Degrees of Freedom (DoFs) for Integrated Sensing And Communication (ISAC) systems, enabling high-resolution Direction of Arrival (DoA) estimation. In highly dynamic scenarios, however, DoA estimation must be performed within short coherence intervals, which often restricts processing to a single snapshot. Conventional subspace methods then suffer from rank deficiency, while Hankel-based spatial smoothing incurs high computational cost when the array size is large. This paper proposes a low-complexity gridless Truncated Hankel NewtonMUSIC framework for single-snapshot DoA estimation. The proposed method constructs a truncated Hankel matrix with a fixed row dimension to recover an effective signal subspace while reducing the cost of correlation construction and subspace decomposition. When the truncation length is independent of the array size, the dominant complexity scales linearly with the number of antenna ports. To reduce grid-induced quantization errors, coarse grid estimates are further refined by a secondorder Newton update in the continuous angular domain. Simulation results show that the proposed method achieves DoA estimation accuracy close to square Hankel Newton-MUSIC while substantially reducing runtime. For large arrays, it provides more than two orders of magnitude runtime reduction compared with conventional square Hankel MUSIC, making it suitable for realtime sensing in reconfigurable antenna-enabled ISAC systems.