TY - JOUR
T1 - Bearing-Only Localization for Wideband Off-Grid Sources with Distributed Sensor Array Networks
AU - Wu, Hantian
AU - Shen, Qing
AU - Liu, Wei
AU - Zhou, Junwei
AU - Cui, Wei
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2026
Y1 - 2026
N2 - In this paper, a study on bearing-only localization for wideband off-grid sources is presented by employing distributed sensor array networks. First, the wideband off-grid signal models based on distributed sensor array networks for both far-field and near-field scenarios are established with coarse grids employed for complexity reduction, where focusing algorithm is applied as a preprocessing step to facilitate multiple-frequency signal processing. Then, both one-step and two-step group-sparsity-based (GS-based) estimation methods are proposed for the considered two scenarios, where the on-grid information and the off-grid bias are recovered jointly (one-step methods) or alternatively (two-step methods). The proposed methods are capable of handling both overdetermined and underdetermined cases, leveraging information collected from all distributed receivers. To further alleviate off-grid approximation and focusing errors, a dynamic-dictionary-based iterative refocused grid-refining strategy is introduced, and the Cramér-Rao Bounds (CRBs) for uncorrelated source localization are also derived, which exists in the underdetermined case with more sources than subarray sensors. Simulation results demonstrate that improved performance can be achieved by our proposed off-grid solutions compared to existing methods.
AB - In this paper, a study on bearing-only localization for wideband off-grid sources is presented by employing distributed sensor array networks. First, the wideband off-grid signal models based on distributed sensor array networks for both far-field and near-field scenarios are established with coarse grids employed for complexity reduction, where focusing algorithm is applied as a preprocessing step to facilitate multiple-frequency signal processing. Then, both one-step and two-step group-sparsity-based (GS-based) estimation methods are proposed for the considered two scenarios, where the on-grid information and the off-grid bias are recovered jointly (one-step methods) or alternatively (two-step methods). The proposed methods are capable of handling both overdetermined and underdetermined cases, leveraging information collected from all distributed receivers. To further alleviate off-grid approximation and focusing errors, a dynamic-dictionary-based iterative refocused grid-refining strategy is introduced, and the Cramér-Rao Bounds (CRBs) for uncorrelated source localization are also derived, which exists in the underdetermined case with more sources than subarray sensors. Simulation results demonstrate that improved performance can be achieved by our proposed off-grid solutions compared to existing methods.
KW - Cramér-Rao bound
KW - distributed sensor array networks
KW - group sparsity
KW - Off-grid localization
KW - underdetermined
UR - https://www.scopus.com/pages/publications/105027963282
U2 - 10.1109/JSEN.2026.3651323
DO - 10.1109/JSEN.2026.3651323
M3 - Article
AN - SCOPUS:105027963282
SN - 1530-437X
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
ER -