Adaptive Matched Field Processing for Source Localization Using Improved Diagonal Loading Algorithm

Abstract

Matched field processing (MFP) has been a method widely applied for shallow underwater target localization, which is a critical issue in underwater acoustic. To enhance the efficiency of conventional MFP methods, different adaptive MFP algorithms have been developed; the white noise constraints (WNC) MFP or diagonal loading (DL) algorithm is such a typical one. The WNC or DL one has been considered to be the most desirable method because it is more robust to environment mismatch in practical in comparison with the minimum-variance distortionless response MFP algorithm, a popular high-resolution method. Although having exceptional ability to localize underwater sources in mismatch scenarios, the DL method has still been not reach high resolution in certain cases. In the paper, we proposed an adaptive method known as improved diagonal loading algorithm to make an increase in the resolution and the peak background rate in the ambiguity surface of source localization results in comparison with DL one. The proposed algorithm works by adding one more parameter that is adjusted in the steering vector of the DL algorithm. The simulation results show that the new algorithm attains better beamforming performance in terms of high resolution than the existing adaptive MFP algorithms in the case of environmental mismatch caused by noise effects and the limitation of the snapshots.