Improved channel tracking in underwater systems using time-varying sliding window RLS with dual projection framework

The Recursive Least Squares (RLS) algorithm is widely used for channel estimation, but its performance degrades in dynamic and noisy underwater environments. To address this issue, we propose an enhanced RLS variant, the Time-Varying Sliding Window RLS (TVSRLS) algorithm. The TVSRLS algorithm extracts the signal's frequency features using the Chirplet Transform. The window length is then dynamically adjusted based on changes in the signal's frequency. Using a rotation matrix, the algorithm projects the signal along the direction with the highest Signal-to-Noise Ratio (SNR), optimizing sensitivity to relevant signals. The window shape is adaptively scaled in that direction using a variable window length and an anisotropic operator. This approach suppresses noise from other directions, further improving SNR. The algorithm applies a second projection using Local Basis Functions to map the signal into the local time-frequency domain. This local time-frequency processing reduces residual noise, further improving signal clarity. Simulations demonstrate that TVSRLS consistently outperforms the traditional Sliding window RLS (SRLS) in various noise conditions, providing more accurate channel estimation.