ZA-APA with Adaptive Zero Attractor Controller for Variable Sparsity Environment

The zero attraction affine projection algorithm (ZA-APA) achieves better performance in terms of convergence rate and steady state error than standard APA when the system is sparse. It uses l1 norm penalty to exploit sparsity channel. ZA-APA depends on value attractor controller. Moreover a fixed controller not suitable for varying environment. This paper proposes an optimal adaptive based Mean Square Deviation (MSD) work variable Experiments were conducted prove suitability proposed identification unknown sparse system.