This paper delves into the utilisation of the subgradient method with geometrically decaying stepsize for Bilinear Form Identification. We introduce the iterative Wiener Filter, an l2 regression method, and highlight its limitations when confronted with noise, particularly heavy-tailed noise. To address these challenges, the paper suggests employing the l1 regression method with a subgradient method utilizing a geometrically decaying step size. The effectiveness of this approach is compared to existing methods, including the ALS algorithem. The study demonstrates that the l1 algorithm, especially when paired with the proposed subgradient method, excels in stability and accuracy under conditions of heavy-tailed noise. Additionally, the paper introduces the standard rounding procedure and the S-outlier bound as relaxations of traditional assumptions. Numerical experiments provide support and validation for the presented results.