A modified block FTF adaptive algorithm with applications to underwater target detection

In this paper, the problem of weighted block recursive least squares (RLS) adaptive filtering is formulated in the context of a block fast transversal filter (FTF) algorithm. This "modified block FTF algorithm" is derived by modifying the constrained block-LS cost function to guarantee global optimality. This new soft-constrained algorithm provides an efficient way of transferring weight information between blocks of data. The tracking ability of the algorithm can be controlled by varying the block length and/or a soft constrained parameter. This algorithm is computationally more efficient compared with other LS-based schemes. The effectiveness of this algorithm is tested on a real-life problem dealing with underwater target identification from acoustic backscatter. The process involves the identification of the presence of resonance in the acoustic backscatter from a target of unknown shape submerged in water.