Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Advanced Engineering Mathematics: Maple Computer Guide
Advanced Engineering Mathematics: Maple Computer Guide
Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
State-space recursive least-squares: part I
Signal Processing - Special section: New trends and findings in antenna array processing for radar
Adaptive tracking of linear time-variant systems by extended RLSalgorithms
IEEE Transactions on Signal Processing
Digital Signal Processing
ACC'09 Proceedings of the 2009 conference on American Control Conference
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State-space recursive least-squares (SSRLS) enhances the tracking ability of the standard recursive least-squares (RLS) by incorporating the underlying model of the environment. Its overall performance, however, depends on model uncertainty, presence of external disturbances, time-varying nature of the observed signal or nonstationary behavior of the observation noise. It turns out that the forgetting factor plays an important role in this context. However, depending on the problem, it may be difficult or even impossible to have a prior estimate of the best value of forgetting factor. As a logical approach to such situations, SSRLS with adaptive memory (SSRLSWAM) is developed in this paper. This in turn has been achieved by stochastic gradient tuning of the forgetting factor. An approximation based on steady-state SSRLS is also derived. The resultant filter alleviates the computational burden of the full-fledged algorithm. An example of tracking a noisy chirp demonstrates the overall capability and power of the new algorithm. It is expected that this new filter will be able to track and estimate time-varying signals that are difficult to handle with the available tools.