Research and application of intelligent vector signal error calibration method

Traditional vector signal calibration algorithms mostly use superposition algorithms that rely on vector signal characteristics or adaptive algorithms based on feedback systems. This article analyzes the calibration algorithms based on the adaptive model and adds intelligent modules to improve the calibration ability and operation performance of the model. Besides, this article proposes an artificial intelligence-based I/Q signal calibration scheme based on the principle of deep learning. This method introduces the recurrent neural network into the calibration process of the I/Q signal. Through the analysis of performance evaluation indicators and the optimization of the adaptive model, calibration network model, automatic calibration of the I/Q signal imbalance problem is achieved. Through intelligent dynamic adjustment strategies, the stability and reliability of measurements are improved, thereby improving the performance of the vector signal analyzer in processing I/Q signals in complex environments.