To address the challenge of target tracking for non-maneuvering, single-station setups in long-range scenarios, we propose a target tracking algorithm leveraging three-dimensional angle of arrival data, characterized by its asymptotically unbiased nature. Initially, we construct a motion and observation model centered on a non-maneuvering single station, assuming a known rate prior, and examine the system's observability. To tackle the bias inherent in the pseudo linear least squares algorithm, we introduce a constrained total least squares method that demonstrates asymptotically unbiased properties, with its effectiveness validated through simulations. In tests involving three-dimensional angle tracking over distances in the hundred-kilometer range, with angle measurement standard deviations at 0.1°, 0.2°, and 0.3°, the constrained total least squares method achieves a time-average relative distance error of 6%, 12%, and 21% within 50-100 seconds, respectively, and an absolute position error of 9 km, 19 km, and 35 km; at initial distances of 70, 140, and 280 km, the errors are 1%, 6%, and 30% for the same duration, with absolute errors of 0.7 km, 9 km, and 30 km. Notably, the relative distance error can be reduced to below 10% within 100 seconds, marking a significant precision enhancement, while maintaining operational speed comparable to the pseudo linear least squares method. The constrained total least squares approach exhibits rapid convergence, high accuracy, and swift processing, showing resilience against angle measurement errors and initial distance variations. It offers a robust solution for 3D angle of arrival tracking of non-maneuvering single-station targets in distant settings.