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Among many transform-domain interference excision techniques, transform-domain adaptive filtering has many advantages. It is based on a true optimization of some particular performance parameters such as the bit-error rate (BER). Moreover, it is insensitive to jammer frequency. However, transform-domain adaptive filtering also has the drawback of being incapable of tracking a rapidly changing interference because most adaptive algorithms require time to converge to the optimal solution. In this paper, a self-orthogonalizing transform-domain least mean square (SO-TRLMS) algorithm is used to speed up the convergence. Compared to a traditional transform-domain least mean square (TRLMS) algorithm, the SO-TRLMS algorithm can significantly improve the convergence rate of the LMS algorithm, thus making the transform-domain adaptive filtering technique more suitable for real-time processing. In order to show how the system performance is affected by various factors such as interference power and the transform used, this paper presents an analytical result for the BER performance that is applicable for arbitrary orthogonal linear transforms. Simulation results are also presented to demonstrate the validity of the analysis.