In order to measure absolute distances between multiple targets over a wide range, a measurement estimation method is designed which is based on a fusion strategy of temporal features and nonlinear fitting of satellite receiver positions. The method takes the undifferential-corrected latitude and longitude data collected by a low-cost satellite receiver through wavelet denoising, and then realizes the accurate distance estimation of the measurement target through the fitted regression model designed to fuse the Temporal Convolutional Network (TCN) and the Fully Connected network (FC).To enable model training, a dataset featuring deep fusion characteristics is constructed, while employing a Genetic Algorithm (GA) to optimize the weights and thresholds of the model’s fully connected layer, thereby enhancing its nonlinear regression capability. The method also makes up for the defect of the Haversine formula for latitude and longitude distance calculation, which has a high demand for localization accuracy. The experimental results show that the neural network model designed and optimized in this paper is better than the other six models in terms of performance, which fully verifies that the method has good data fitting ability and prediction accuracy, and is practical in the static distance measurement problem.