표 2. | Table 2. 비정상성 신호에 대한 빔포밍 기법 | Data-dependent beamformers for non-stationary signals.

	LMS-based beamforming	RLS-based beamforming
Objective function	J [ k ] = E { | d [ k ] − w H [ k ] y [ k ] | 2 }	J [ k ] = ∑ ν = 0 k β v | d [ k − ν ] − w H [ k − ν ] y [ k − ν ] | 2
Design method	w [ k + 1 ] = w [ k ] + μ ( d [ k ] − w H [ k ] y [ k ] ) * y [ k ] w [ 0 ] = 0	w [ k + 1 ] = w [ k ] + R y − 1 [ k ] y [ k ] ( d [ k ] − w H [ k ] y [ k ] ) * w [ 0 ] = 0
Complexity	O(N)	O(N2)
Operation property	The LMS-based algorithm minimizes the weighted sum of the squared error for stochastic signals. The speed of convergence of the LMS-based algorithm depends on the step-size and the eigenvalue spread of Ry(0 < μ < 2/λmax, λmax denotes the largest eigenvalue of Ry)	The RLS-based algorithm minimizes the weighted sum of the squared error for deterministic signals. The speed of convergence of the RLS-based algorithm does not depend on the condition number of Ry, which leads to, in general, an order of magnitude faster than that of the LMS-based algorithm.