Key result
The proposed tracking-based hybrid calibration approach using an extended Kalman filter substantially outperformed the existing NLMS method in mismatch estimation and signal reconstruction for time-interleaved ADCs.
Why the study?
Mismatch errors between sub-ADCs degrade signal quality and represent a main performance bottleneck in time-interleaved ADCs.
A novel tracking-based hybrid calibration approach using an extended Kalman filter significantly improves mismatch estimation and signal reconstruction in time-interleaved ADCs compared to existing methods.
Hybrid EKF calibration enables real-time TI-ADC mismatch correction; leaves open hardware validation before clinical signal-acquisition use.
Time-interleaved analog-to-digital converters (TI-ADCs) can achieve high sampling rates by interleaving multiple sub-ADCs in parallel. Mismatch errors between the sub-ADCs, however, can significantly degrade the signal quality, which is a main performance bottleneck in TI-ADCs. In this letter, we present a hybrid calibration approach by interpreting the mismatch problem as a tracking problem, and use the extended Kalman filter for online estimation of the mismatch errors. After estimation, the input signal is reconstructed using a truncated fractional delay filter and a high-pass filter. Simulations demonstrate that our algorithm substantially outperforms the existing hybrid calibration method in both mismatch estimation and compensation.
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Jinseok Choi (2025) studied this question. Tracking-Based Hybrid Calibration Approach (Extended Kalman Filter) vs. NLMS algorithm (existing hybrid calibration method) was evaluated on Mismatch estimation and signal reconstruction (Normalized Mean Square Error and Bit Error Rate). The proposed tracking-based hybrid calibration approach using an extended Kalman filter substantially outperformed the existing NLMS method in mismatch estimation and signal reconstruction for time-interleaved ADCs.
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