Linearly-constrained line-search algorithm for adaptive filtering

A linearly-constrained line-search adaptive filtering algorithm has been developed by incorporating the linear constraints into the least squares problem and searching the solution (filter weights) along the Kalman gain vector. The proposed algorithm performs close to the constrained recursive least squares algorithm while having a computational complexity comparable to the constrained least mean square algorithm. Simulations demonstrate its effectiveness.