The steered covariance matrix(STCM) and its inverse matrix should be calculated in each beam for steered minimum variance(STMV) . The inverse matrix needs complex computation and restricts its application in engineering. Combining the integration character of one-phase regressive filter with the iterative formula of inverse matrix,an STMV iterative algorithm is proposed. The computational cost of the iterative algorithm is reduced approximately to be 2/M times of the original one when there are M sensors,and is more advantaged for the realization of the algorithm in real time. Simulation results show that the STMV iterative algorithm can preserve the characters of STMV on high azimuth resolution and weak target detection while the computational cost reduced sharply. The analysis on sea trial data proves that the proposed algorithm can estimate each target’s azimuth even when the source powers differ in large scales or their bearings are very approximate. The steered covariance matrix (STCM) and its inverse matrix should be calculated in each beam for steered minimum variance (STMV). The inverse matrix needs complex computation and restricts its application in engineering. Combining the integration character of one-phase regressive filter with the iterative formula of inverse matrix, an STMV iterative algorithm is proposed. The computational cost of the iterative algorithm is reduced approximately to be 2 / M times of the original one when there are millions of sensors, and is more advantaged for the realization of the algorithm in real time. Simulation results show the the STMV iterative algorithm can preserve the characters of STMV on high azimuth resolution and weak target detection while the computational cost reduced sharply. The analysis on sea trial data proves that the proposed algorithm can estimate each target’s azimuth even when the source powers differ in large scales or their bearings are very approximate.