A very robust, fast, parallelizable adaptive least squares algorithm with excellent tracking abilities

In this paper a new computational scheme is introduced for performing recursive least squares adaptive filtering. The proposed algorithm is far more robust than all the already existing RLS schemes, in the sense that it is drastically less sensitive in the numerical error due to the finite precision with which all operations are executed. Hence, it has a lifetime tens of times greater than all the previous RLS schemes. Moreover, the algorithm introduced here has excellent tracking abilities and, due to its particular structure, it is parallelizable. When it is executed in parallel by four processors, it is faster than all the existing RLS algorithms, and in particular, it is by m steps faster, where m is the system order, than the FAEST and the FTF computational schemes