Time Delay Estimation Using RLS Algorithm

MATLAB 688B 256 views 0 downloads 1 credits
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Resource Overview

Time delay estimation through RLS (Recursive Least Squares) demonstrates rapid convergence and maintains estimation accuracy even under low signal-to-noise ratio conditions, featuring adaptive filter implementation with recursive parameter updates.

Detailed Documentation

In network communications, time delay estimation represents a critical task. Traditional time delay estimation algorithms often suffer from slow convergence rates and sensitivity to factors like signal-to-noise ratio, leading to inaccurate estimations. However, more efficient algorithms have emerged, such as employing RLS (Recursive Least Squares) for time delay estimation. The RLS algorithm implements an adaptive filtering approach where filter coefficients are recursively updated using a forgetting factor to prioritize recent data. Compared to conventional algorithms, RLS not only achieves faster convergence through its recursive computation mechanism that avoids matrix inversion, but also delivers more accurate time delay estimates even in low signal-to-noise ratio scenarios. This makes RLS-based time delay estimation increasingly prevalent in network communication systems, particularly suitable for real-time applications requiring continuous parameter tracking.

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