Tensor-based time-delay estimation for second and third generation global positioning system

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

“…In [16], the tensor-based methods proposed in [10], [14], [15] were extended to third-generation GPS (GPS3) signals, since GPS3 is more robust against multipath components in comparison to the signals of the second-generation GPS (GPS2) due to its Time Multiplexed Binary Offset Carrier (TMBOC) modulation [17]- [19]. In [20], the authors showed that the SEmi-algebraic framework for computing an approximate CP decomposition via Simultaneous Matrix Diagonalizations (SECSI) [21] can be applied to GPS2 and GPS3 signals and antenna array-based receivers using GPS3 and SECSI outperform antenna array receivers using GPS2 and SECSI. This underlines the superior multipath performance of the new GPS3 signals.…”

“…This underlines the superior multipath performance of the new GPS3 signals. Additionally, [20] showed that the SECSI method is more robust in the presence of strongly correlated signals than the CPD-GEVD method while on the other hand being computationally more expensive.…”

“…The Tensor-based Eigenfilter [10] does not require a previous model order selection (MOS), but achieves lower accuracy compared to the methods proposed in [15], [20]. However, the CPD-based techniques, introduced in [15], [20], require an estimate of the tensor model order to en-able tensor factorization.…”

“…The Tensor-based Eigenfilter [10] does not require a previous model order selection (MOS), but achieves lower accuracy compared to the methods proposed in [15], [20]. However, the CPD-based techniques, introduced in [15], [20], require an estimate of the tensor model order to en-able tensor factorization. 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.…”

“…However, the CPD-based techniques, introduced in [15], [20], require an estimate of the tensor model order to en-able tensor factorization. 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.…”