IQ mismatch correction algorithm based on kalman filter

Abstract:

To improve the convergence speed and robustness of in-phase quadrature (IQ) imbalance correction in zero-IF receivers, this study integrates the Kalman filtering algorithm with a blind source separation structure and proposes a dual-channel Kalman filter-based correction method. By leveraging state-space modeling and adaptive covariance updates, the proposed algorithm enables more efficient and stable parameter estimation in dynamic environments, thereby achieving effective compensation for IQ mismatch. Comparative simulations were conducted between the proposed algorithm and the least mean square (LMS), normalized least mean square (NLMS), and affine projection algorithm (APA). The results show that the image rejection ratio (IRR) of the corrected signals reaches approximately 45 dB for all methods. However, the IRR surface of the proposed dual-channel Kalman filtering algorithm is smoother. Additionally, under 16QAM and 16PSK modulation schemes, the proposed algorithm yields the lowest symbol error rate (SER), indicating improved correction performance and stability. The algorithm also demonstrates superior convergence, with its mean squared error (MSE) approaching zero after approximately 50 iterations, while LMS, NLMS, and APA require about 0,0, and 200 iterations, respectively, to converge. Furthermore, sensitivity analysis reveals that the IRR variation remains minimal across a wide range of parameter settings, demonstrating the robustness of the proposed algorithm.