To suppress interference from discrete noise of underwater sound sources, we apply matched field processing with multiple-constraint noise suppression (MNS MFP) to a distributed multisensor array. Optimal weight vectors and coherent processing are also combined with MNS MFP. The results from simulations clearly show the effectiveness of the method to localize and differentiate noise and sound sources. The probability of successful sound source localization (SSL) for the new method is higher than 0.9 with an error range of 5% for depth and distance. In addition, it accurately locates a sound source and a noise source at the same distance from the multisensory array at a depth difference of 33 m. This localization ability is much improved from that of the conventional matched field processing (CMFP) and diagonal loading minimum variance distortionless response (DL-MVDR) methods. With further development, the proposed method is expected to provide more accurate and precise data for underwater acoustics studies.