Mixed H2/ H∞ algorithm for exponentially windowed adaptive filtering
The RLS or its equivalent H<inf>2</inf> algorithms achieve the best average performance. But these suffer from poor worst case performance. Stochastic gradient based algorithms (like LMS) achieve best worst case performance, but a poor average performance. In this paper we propose switching criteria for acquiring advantages of both of these algorithms. The proposed algorithm uses a nonlinear combination of H<inf>2</inf> optimal and H<inf>∞</inf> optimal estimation. We present a mixed H<inf>2</inf>/H<inf>∞</inf> algorithm employing an exponential window and the achievable bound for this estimation strategy.