A data-driven nonlinear control approach, called error dynamics-based dual heuristic dynamic programming (ED-DHP), is proposed for air vehicle attitude control. To solve the optimal tracking problem, augmented system defined by derived dynamics and reference trajectory so that actor neural network can learn feedforward feedback terms at same time. During online self-learning process, learns pol...

In this paper, adaptive pilot-aided channel estimation for orthogonal frequency-division multiplexing (OFDM) transmission is addressed. For this purpose, instead of a theoretically optimum two-dimensional (2D) filter, two consecutive one-dimensional (2× 1D) filters are applied. In particular, three algorithms for the first 1D step are compared, namely normalized least-mean-squares (NLMS), norma...

Recently, the decision feedback channel estimation based on the minimum mean square error criterion (DF-MMSE-CE) using a fixed DF filter coefficient has been proposed to improve the channel estimation accuracy for DS-CDMA with frequency-domain equalization (FDE). In this paper, we propose adaptive DF (ADF)-MMSE-CE, in which the DF filter coefficient is adapted to changing channel conditions bas...

Traditional recursive least squares (RLS) adaptive filtering is widely used to estimate the impulse responses (IR) of an unknown system. Nevertheless, RLS estimator shows poor performance when tracking rapidly time-varying systems. In this paper, we propose a multi-layered (m-RLS) address concern. The m-RLS composed multiple estimators, each which employed and eliminate misadjustment previous l...

In this paper, a filtered-x recursive least squares (FX-RLS) algorithm based on adaptive 3rd-order Volterra filtering is proposed for nonlinear active noise control (ANC) of systems with a nonlinear primary acoustic path. The simulation results show that the proposed approach yields faster convergence, compared with the conventional methods (e.g., FX-NLMS and FX-GSPAP), for the nonlinear ANC. K...

Quadratic Rotation decomposition (QRD) based recursive least squares (RLS) algorithm can be used in variety of communication applications and its low complexity implementation can be of interest. In this paper we have presented FPGA implementation of QRD based RLS algorithm using Coordinate Rotation by Digital Computer (CORDIC) operator. FPGA resource estimates along with actual implementation ...

This paper considers an extended recursive least squares (RLS) adaptive bilinear predictor. It is shown that the extended RLS adaptive bilinear predictor is guaranteed to be stable in the sense that the time average of the squared a-posteriori prediction error signal is bounded whenever the input signal is bounded in the same sense. It also shows that the a-priori prediction error itself is bou...

In an earlier paper, a systolic algorithm/array was derived for recursive least squares (RLS) estimation, which achieves an O(n0) throughput rate with O(n2) parallelism. The resulting array is specifically tuned towards the RLS problem. Here, a different route is taken, by trying to implement the RLS problem on a systolic array, which is also useful for several other applications, such as, e.g....

In this paper, first a brief review is given of a fully pipelined algorithm for recursive least squares (RLS) estimation, based on socalled ‘inverse updating’. Then a specific class of (block) RLS algorithms is considered, which embraces normalized LMS as a special case (with block size equal to one). It is shown that such algorithms may be cast in the ‘inverse-updating RLS’ framework. This all...

Kalman filter is linear optimal estimator for random signals. We develop state-space RLS that is counterpart of Kalman filter for deterministic signals i.e. there is no process noise but only observation noise. State-space RLS inherits its optimality properties from the standard least squares. It gives excellent tracking performance as compared to existing forms of RLS. A large class of signals...