In the networked high precision measurement and localization, the requirements of growing measurement scale, enlarging communication constraints and increasing signal coordination processing bring great challenges in this domain. Based on the spatial localization principle, the information fusion estimation is investigated for uncertain networked systems with crosscorrelated noises and data transmission delays. A distributed fusion estimation scheme is proposed by distributed perception and centralized fusion based on Kalman filtering. The scheme designs an optimal weighted fusion estimator employing the measurement transformation and the twostage weighted fusion approaches. As a result, the communication burden and computational cost with networkinduced transmission delays can be alleviated, and the noisy disturbances can be decomposed, and robustness can be improved. Moreover, information redundancy can be reduce and the higher measurement accuracy can be maintained. An illustrative example is given to validate the effectiveness of the proposed method.