An adaptive order-selection autoregressive pre-whiten filtering in active sonar target detection with reverberation cancellation ability

Abstract

There are many proposed methods for active sonar target echo detection in reverberation background via either optimal or suboptimal signal processing procedures. Among such methods, those ones which are based on mere filtering, e.g. matched filter, are both efficient and flexible, but suffer from some deficiencies such as high computational complexity or more additional requirements for post-processing. In this paper, an adaptively order-selected pre-whiten filtering method based on autoregressive (AR) modeling of the reverberation data at the active sonar receiving hydrophone is proposed. This method is able to overcome the deficiencies of former filtering methods. This is achieved by applying an AR pre-whiten filter that has its order selected adaptively using data partitioning. The adaptive order selection of AR pre-whiten filter is done by the use of FPEF which is a high performance AR order selection criterion. The results of simulation show that the proposed method is more efficient than the previously proposed order/reverse partition AR pre-whiten algorithm in the sense of echo-to-reverberation ratio (ERR).