Non-periodic-partial-update affine projection algorithm with data-selective updating

This paper proposes a non-periodic-partial-update affine projection algorithm with data-selective updating. The proposed algorithm employs two update concepts: non-periodic partial update and data-selective update. The former plays a role in adjusting the length of the update period, and the latter in reducing computational complexity. Thus, the algorithm requires two key procedures of length assignment and state decision. The length assignment procedure determines the length of the update period by checking whether the current input vectors have enough information with an update period assignment criterion. The state decision procedure stochastically determines whether the adaptive filter has reached a steady state. When the current state of the adaptive filter is confirmed as a transient state by the decision procedure, the algorithm updates all filter coefficients with an update period assigned by the length assignment procedure. Through these two procedures, the proposed algorithm not only achieves good performance, especially for colored input signals, in terms of the convergence rate and steady-state estimation errors but also provides a substantial reduction in the number of updates.