In order to improve the accuracy of radar system parameter estimation， this paper proposes a data fusion and parameter fusion method for parameter estimation based on Bayesian principle. We derive the distance information， entropy error and mean square error （MSE） for a multi-radar system under additive complex Gaussian noise conditions， and derive an upper bound on the distance information. The theoretical derivation shows that the maximum a posteriori estimate （MAP） of the position estimation is consistent with the maximum ratio of the position information. The equivalent signal-to-noise ratio of the multi-radar system is equal to the sum of the signal-to-noise ratios of radars in the system. Experimental results indicate that， in general， the performance of data fusion is always superior to that of parameter fusion. However， data fusion relies on the assumption of uniform distribution and requires distortion-free acquisition of the received signals from all nodes， representing an idealized scenario. In contrast， parameter fusion is more aligned with real-world scenarios， and its estimation accuracy is not significantly inferior to that of data fusion. The findings of this study provide valuable guidance for improving the accuracy of target parameter estimation in practical environments.