在深空探测中探测信号经过对流层延迟后在接收机端信号将出现一定程度的时延，影响探测精度。现有方法主要通过网格模型、空间模型实现时延预测，但由于区域差异导致模型准确度受限，预测精度仍有改进空间。提出了一种基于自适应多输入多输出（MIMO）信号的深空探测对流层延迟预测模型。基于单一收发天线模拟卫星信号MIMO传输方式，然后构建自适应卡尔曼滤波器，通过自适应调整MIMO信号分量权重系数的方法选取最优传输路径以实现对流层延迟量的预测。参与测量的卫星数目为4颗，在不同信噪比以及改变MIMO通道数目情况下开展实验，研究自适应MIMO模型的准确度和实际测量误差。实验结果表明，新方法相对于GPT2模型、GPT2w模型以及实时导航定位中常用的UNB3模型、EGNOS模型的预测精度有较大提高。

In the deep space exploration, the signal is delayed by the troposphere and a certain delay will occur at the receiver end, which affects the detection accuracy. The existing methods mainly realize the delay prediction by the grid model and the space model, but the accuracy of the model is limited due to the regional difference, and the prediction accuracy still has room for improvement. In this paper, a deep-space tropospheric delay prediction model based on adaptive multiple-input multiple-output (MIMO) signals was proposed. The satellite signal MIMO transmission mode was simulated based on a single transceiver antenna, and then an adaptive Kalman filter was constructed. The optimal transmission path was selected by adaptively adjusting the weight coefficient of the MIMO signal component to predict the tropospheric delay. The number of satellites participating in the measurement was four. Experiments were carried out under different signal-to-noise ratios and changing the number of MIMO channels to study the accuracy and actual measurement error of the adaptive MIMO model. The experimental results show that the prediction accuracy of the new method is much higher than that of the GPT2 model, GPT2w model and the commonly used UNB3 model and EGNOS model in real-time navigation and positioning.