We study the distributed Kalman filter in sensor networks where multiple sensors collaborate to achieve a common objective. Diffusion Kalman filtering algorithms have been a popular topic in linear dynamic system estimation problems, In there algorithms, nodes cooperate with their direct neighbors and diffuse the information across the entire network through a sequence of Kalman iterations and data-aggregation. In this article, we propose the diffusion Kalman filtering with quantized global (DKFQFI)algorithm because of the limited sources in the wireless environment, where nodes exchange their quantized states with neighbors to reduce the consumption of resources. To prove the convergence of the DKFQFI algorithm, we derive the theoretical expressions of the mean and mean-square performance. From the expressions, we show that the mean performance and mean-square performance of the proposed algorithm are unbiased and stable. Therefore, the feasibility of the algorithm is verified. Moreover, the proposed algorithm achieves an outperform by simulation.