Low-Complexity Phase Ambiguity Resolution DOA EstimationAlgorithm for Composite Hierarchical Receiving Array Structure

CHEN Yiwen; DONG Yangze; CHEN Xiahua; LING Wenchang; XIONG Yiwen

doi:10.11999/JEIT260447

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CHEN Yiwen, DONG Yangze, CHEN Xiahua, LING Wenchang, XIONG Yiwen. Low-Complexity Phase Ambiguity Resolution DOA EstimationAlgorithm for Composite Hierarchical Receiving Array Structure[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260447

Citation:

CHEN Yiwen, DONG Yangze, CHEN Xiahua, LING Wenchang, XIONG Yiwen. Low-Complexity Phase Ambiguity Resolution DOA EstimationAlgorithm for Composite Hierarchical Receiving Array Structure[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260447

Citation:

CHEN Yiwen, DONG Yangze, CHEN Xiahua, LING Wenchang, XIONG Yiwen. Low-Complexity Phase Ambiguity Resolution DOA EstimationAlgorithm for Composite Hierarchical Receiving Array Structure[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260447

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Low-Complexity Phase Ambiguity Resolution DOA EstimationAlgorithm for Composite Hierarchical Receiving Array Structure

doi: 10.11999/JEIT260447 cstr: 32379.14.JEIT260447

Guangdong Provincial Laboratory of Southern Ocean Science and Engineering (Zhanjiang), Zhanjiang 524000, China

Received Date: 2026-04-15
Accepted Date: 2026-06-24
Rev Recd Date: 2026-06-22

Available Online: 2026-07-04

Abstract

Abstract

Objective Direction Of Arrival (DOA) estimation is a key technique for sonar target localization. As the demand for high-precision DOA estimation in complex environments continues to increase, the number of array elements used for estimation is steadily growing, leading to massive arrays. Although larger arrays improve DOA estimation accuracy and resolution, they also impose a substantial computational burden on conventional DOA estimation algorithms. To address this issue, a low-complexity composite hierarchical receiving array structure is constructed, and two fast phase ambiguity resolution algorithms are proposed: Composite HierArchical Global Nearest-Neighbor Matching (CHA-GNNM) and Composite HierArchical Cross-Correlation Covariance Merging (CHA-CCM). Methods The CHA-GNNM algorithm constructs multiple candidate solution sets by exploiting the auto-covariance and cross-covariance relationships among the subarrays within each group. The true solution in each candidate solution set is identified through nearest-neighbor matching based on source consistency, and the final DOA estimate is obtained through multilevel coherent combining. This approach achieves phase ambiguity resolution and angle matching with relatively low computational cost. However, because the correlation information among all array elements is not fully exploited, some estimation performance is sacrificed. To improve DOA estimation performance, the CHA-CCM algorithm reorganizes the composite hierarchical structure into evenly partitioned groups, which are regarded as several large subarrays. Multiple large candidate solution sets are first constructed from the cross-correlation relationships among these groups. Each group is then divided into multiple small subarrays, from which additional candidate solution sets are generated using the corresponding auto-covariance and cross-covariance relationships. A coarse DOA estimate is obtained through coprime clustering, followed by a more accurate initial DOA estimate derived from the small candidate solution sets. This initial DOA estimate is subsequently used to eliminate spurious solutions from the large candidate solution sets, yielding the final DOA estimate. Combined with a low-complexity covariance block-processing strategy, this approach avoids computationally expensive operations while improving DOA estimation accuracy. Results and Discussions Simulation results demonstrate that both proposed algorithms substantially reduce the computational burden as the number of array elements increases, while effectively achieving phase ambiguity resolution through the proposed composite hierarchical receiving array structure (Fig. 5). Compared with the conventional Root-MUSIC algorithm, CHA-GNNM achieves coarse DOA estimation with nearly four orders of magnitude lower computational complexity (Fig. 7), making it suitable for applications with stringent real-time requirements. In contrast, CHA-CCM requires only a modest increase in computational cost (Fig. 7) while achieving DOA estimation performance close to the Cramér-Rao Lower Bound (CRLB) above a certain signal-to-noise ratio threshold (Fig. 6). Therefore, a favorable balance is achieved between DOA estimation accuracy and computational complexity. Conclusions To address the rapid increase in computational complexity associated with massive arrays, a composite hierarchical receiving array structure is constructed for efficient DOA estimation. By hierarchically grouping the array elements, the proposed structure provides a new framework for low-complexity DOA estimation. Based on this structure, two fast DOA estimation algorithms are developed. Both algorithms achieve effective phase ambiguity resolution with low computational complexity by exploiting the structural differences among array groups and the consistency of observations from the same source across different groups, thereby enabling rapid DOA estimation. CHA-GNNM primarily exploits the phase relationships among subarrays to perform phase ambiguity resolution and angle matching through a simple computational procedure, making it suitable for applications requiring high computational efficiency and real-time processing. Because the cross-correlation information among all array elements is not fully exploited, some estimation performance is reduced under challenging signal conditions. To overcome this limitation, CHA-CCM reorganizes the composite hierarchical receiving array into evenly partitioned groups while preserving the low-complexity advantage of the hierarchical structure. Group-level cross-correlation information is further exploited so that the intrinsic relationships among different groups are more fully utilized. In addition, the signal processing procedure is simplified by eliminating unnecessary computational steps, thereby improving the robustness and accuracy of DOA estimation while maintaining manageable computational complexity. Compared with CHA-GNNM, CHA-CCM incurs only a small increase in computational cost and achieves a better balance between computational complexity and DOA estimation performance. Overall, the proposed composite hierarchical receiving array structure and the two fast DOA estimation algorithms provide an effective solution for efficient DOA estimation in massive arrays. CHA-GNNM is more suitable for applications with stringent real-time requirements, whereas CHA-CCM is better suited for applications requiring higher DOA estimation accuracy and robustness. The proposed structure achieves efficient phase ambiguity resolution and accurate DOA estimation and provides both theoretical significance and practical value for engineering applications of massive array signal processing.
- Direction Of Arrival (DOA),
- Massive array,
- Computational complexity,
- Nearest neighbor matching

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References(23)

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