In this paper, we propose an improved DCS algorithm, which is called DCS-improved sparsity
adaptive matching pursuit (DCS-IMSAMP) for estimating DS channel without the knowledge of channel sparsity
It uses the first order statistics calculated from the received signal as well as the a priori information about channel's sparsity
. The estimation error [xi] of the cost function is reduced using the compensation factor [epsilon].
Commonly, it can be assumed that polarimetric TWRI signals share the same sparsity
support over different polarization channels, which means that the position of each point-like target is identical for all polarimetric channels, but its amplitude may be different.
When a priori knowledge of signal and sparsity
is unknown, in order to agilely and robustly find out the spectrum holes not occupied by PUs, this paper presents an enhanced novel nonreconstructed sequential compressed wideband spectrum sensing (NSCWSS) algorithm.
If the sparsity
level K satisfying K < spark (A) is known as a priori, problem (2) can be approximated as follows [11, 12]:
18%, the value of sigma was 1.625 and 1.244 for the HC group and DLB group, respectively.
Notice that l must be calculated to guarantee the attack vector's sparsity
complement a user's rating profile by merging those of trusted users through which better recommendations can be fabricate and the cold start and data sparsity
harms can be better handled.
in this alternate domain can then be exploited in CS based algorithms.
In particular we present a variational formulation based on a new variant of the Mumford-Shah models , where we adopt a sparsity
inducing [l.sub.p]-norm approximation to the total length of the boundaries between parts, which promotes gradient-sparser solutions to our model.
have noted that the nature of sparsity
in high dimensional situation can lead to unstable results .
Since sound sources can be regarded as point sources and the number of the sound sources is quite limited in the positioning space, the sound source localization problem essentially implicates the spatial sparsity
. According to this natural sparsity
, Cevher and Baraniuk modeled the sound source localization problem as a sparse approximation problem  based on the sound propagation model and provided better positioning performance.