On the contrary, the sequential method based on group sparsity is robust to such phase interactions, as it only assumes that a common sparsity support is shared among all available bistatic links.
On the other hand, the two-step sequential method based on group sparsity is robust against such phase discrepancies because the performance of the group sparsity-based method does not directly depend on the coherent combination of the multiple Doppler signatures.
An efficient block-coordinate descent algorithm is developed in Section 4, while tuning issues and uniqueness of the group Lasso solution are addressed in Section 5. Section 6 introduces a non-convex segmentation method based on the SCAD regularization to enhance the sparsity of the solution, which translates to retrieving more precisely the change instants.
Thus, utilizing the sparsity of channels in the delay-Doppler domain, a channel estimation method based on compressed sensing (CS) theory can reduce the number of pilots.
Furthermore, Equation 4 is not the only formed to express the sparsity of x, FOCUSS algorithm is another typical sparse signal recovery method based on the minimum norm least square frame.
The selection of the SCAD sparsity tuning parameter using the AIC and BIC information criteria, generalized cross-validation, and a recently proposed method based on the norm of the error in the solution of systems of linear equations are investigated.
It has been demonstrated that the recovery method based on Kronecker dictionary has much less strict requirements for coherence than the classic matching pursuit method with the same sparsity in regard to signal reconstruction, and it is deduced that the former has the higher successfully recovery bound [28, 45].
Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions.
Choose the method based on the objective.
Leveraging these general results, we explore RSS-fingerprinting methods based on block-sparsity and we prove their efficiency.
In this paper, we start from the setting presented in [9] and we present new methods, based on block-sparsity, that improve the performance of the localization system and make it suitable for practical use.
