It is well known that the MDVR beamforming outperforms the conventional delay-sum beamformer in the sense of noise rejection and bearing resolution. However, the MDVR method requires long observation time to achieve high frequency resolution. The STMV method uses the steered covariance matrix of sensor data, so it has an ability to form an adaptive weight vector from a single time-series snapshot. But it uses the same weight vector across all frequencies. In this paper, we propose an SSMV method. The basic idea of the SSMV method is to decompose a full frequency band into several subbands to acquire a weight vector for each subband, individually. Also the wrap may be divided into several subarrays in order to reduce a computational load and the bandwidth of each subband. Simulations using real sea trial data show that the proposed SSMV method has good performance with short observation time.