A hybrid algorithm is proposed to decrease the integration phase measurement error accumulation in open-loop Doppler receiver which is designed for space orbit determination and positioning applications when cooperated closed-loop system is unavailable. Firstly, adaptive-neuro-fuzzy inference system (ANFIS) module α is implemented for the interrupted closed-loop measurement data prediction. Then to improve the prediction accuracy of the ANFIS module α, an adaptive Kalman filter is employed for data fusion with predicted data from ANFIS module α and open-loop measurement data for complementation and corrections. Meanwhile, ANFIS module β is embedded in the Kalman filter for adaptive error compensation to optimise the filter performance. Finally, the time costly ANFIS computations are accelerated by reconfigurable software–hardware co-design module implemented in the receiver system to improve the computing capability and efficiency of system. Experiment results are analysed to demonstrate the effectiveness of proposed hybrid algorithm. It suppresses error accumulation in open-loop receiver phase measurement by 85.89% compared to the directly integration results when closed-loop system is off-line and has 61.02% enhancement compared to the algorithm without adaptive error compensations. Thus, the whole combinatory system accuracy is improved.