基于Kalman滤波的IQ失配校正算法

IQ mismatch correction algorithm based on kalman filter

关键词:  零中频接收机  IQ失配  Kalman滤波  数字信号处理  镜像抑制比

Key words:  zero-IF receiver  IQ mismatch  Kalman filter  digital signal processing  image rejection ratio

摘要:

为提高零中频接收机中正交(in-phase quadrature,IQ)失配信号校正的收敛速度与鲁棒性,本文将Kalman滤波算法与盲源分离结构结合,提出了一种基于双通道Kalman滤波的校正算法。该算法通过状态空间建模与协方差自适应更新,能够在动态环境下实现更高效、稳定的参数估计,从而实现对IQ失配信号的有效补偿。将本文算法与最小均方算法(least mean square,LMS)、归一化最小均方算法(normalized least mean square,NLMS)和仿射投影算法(affine projection algorithm,APA)进行对比仿真,结果显示,校正后信号的镜像抑制比(image rejection ratio,IRR)均达到约45 dB,但双通道Kalman滤波算法对应的IRR曲面图更加平滑,同时,16QAM和16PSK调制方式下该算法的误符号率最低,表明本文算法能够有效实现IQ失配校正,具有较好的稳定性。本文算法迭代约50次时,均方误差收敛趋近于0,而LMS、NLMS和APA算法则分别需要迭代约500次、400次和200次才能够收敛,表明该算法具有较好的收敛性。通过参数的敏感性仿真分析,在较大的参数范围内本文算法达到的IRR差别甚微,具有良好的鲁棒性。

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.