Improved Generalized Sparsity Adaptive Matching Pursuit Algorithm Based on Compressive Sensing

Algorithm 1

MAOMP algorithm.

	Input:
	Sensing matrix
	Observation vector
	Initial step size
	Constant parameter
	Stop threshold
	Initialization parameter:
	{initialize signal approximation}
	{loop index}
	{initial sparsity estimate}
	{empty preliminary index set}
	{empty candidate index set}
	{empty support index set}
	{residual vector}
	{while loop flag}
	While (∼done)
(1)	Compute the projective set
(2)	Compute the estimated sparsity
	If
	Then return on step (1)
	Else , , , return on step (3)
(3)	Compute a new projective set
(4)	Merge to update the candidate index set
(5)	Get the estimate signal value by least squares algorithm:
(6)	Prune to obtain current support index set
(7)	Update signal final estimate
	residual error:
(8)	check the iteration condition
	If
	quit iteration
	else if
	else
	end
	end
	Output: ( s-sparse approximation of signal )