Robust tensor-based techniques for antenna array-based GNSS receivers in scenarios with highly correlated multipath components

Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios

“…, and k) represent the loading matrices of the CPD of the core tensor (22). In theory, the CPD can be directly applied to Y (d) , to extract the factor matrices.…”

“…Thus, even though [15], [20] have improved the performance of TDE in presence of highly correlated multipath, these methods assume a known and constant model order. Recently, in [22], the authors proposed the (L r , L r , 1)-GEVD approach to perform multi-linear rank-(L r , L r , 1) decomposition by clustering NLOS components and obtaining the LOS component. Meanwhile, in [23], the authors proposed a tensor-based subspace tracking framework to keep track and update the tensor signal subspace.…”

“…Meanwhile, in [23], the authors proposed a tensor-based subspace tracking framework to keep track and update the tensor signal subspace. However, similarly to previous tensor-based methods, [22] and [23] assume that the model order is constant between data epochs. Hence, MOS schemes need to be included and assessed for the application of tensor-based methods for GNSS in order to achieve highly accurate and robust TDE of the LOS signal in case correlated multipath signals are received.…”