Abstract
In this paper we present state-space recursive least-squares (SSRLS) filter. This algorithm is a new addition to the family of RLS filters. We cover core topics like batch processing, recursive updates, initialization and steady state solution, etc. SSRLS is very well-suited to estimate a wide class of deterministic signals corrupted by observation noise. This new filter exhibits excellent tracking performance by overcoming some of the limitations of the standard RLS algorithm. With its state-space formulation and sound mathematical basis, SSRLS is expected to become an important tool in estimation theory, adaptive filtering and control systems.
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