This paper studies the problem of SIMULTANEOUS Sparse APPROXIMATION (SSA). This problem arises in many applications that work with multiple signals maintaining some degree of dependency, e. g., radar and sensor networks. We introduce a new method towards joint recovery of several independent sparse signals with the same support. We provide an analytical discussion of the convergence of our method, called SIMULTANEOUS Iterative Method (SIM). In this study, we compared our method with other group-sparse reconstruction techniques, namely SIMULTANEOUS Orthogonal Matching Pursuit (SOMP) and Block Iterative Method with Adaptive Thresholding (BIMAT), through numerical experiments. The simulation results demonstrated that SIM outperformed these algorithms in terms of the metrics Signal to Noise Ratio (SNR) and Success Rate (SR). Moreover, SIM is considerably less complicated than BIMAT, which makes it feasible for practical applications such as implementation in MIMO radar systems.

The main purpose of this paper is to consider the t-best SIMULTANEOUS APPROXIMATION in fuzzy normed spaces. We develop the theory of t-best SIMULTANEOUS APPROXIMATION in quotient spaces. Then, we discuss the relationship in t-proximinality and t-Chebyshevity of a given space and its quotient space.

Purpose: The purpose of the present paper is to introduce the generalized form of Srivastava-Gupta operators and study their APPROXIMATION properties.Methods: We use analytical method to obtain our results.Results: We have established the rate of convergence, in SIMULTANEOUS APPROXIMATION, for functions having derivatives of bounded variation.Conclusions: The results proposed here are new and have a better rate of convergence.