In traditional estimation strategy for radar’s systematic errors, the radar’s measurement data are transferred to fusion center, and the estimation of system errors obtained by radar measurement is feedback to radars to compensate the new measurement data. This strategy would cause delay error, so it is not suitable for applying in real time calibration. To resolve this problem, a structure of network radar system without bidirectional inputs is built, in which the radar measurement and feedback of system error is not required. A new collaborative registration algorithm for network radar system error is proposed which uses estimation of target state as input, and the Extended Kalman Filter (EKF) is applied in this algorithm. Through the simulation, the estimation precision of bearing system errors is achieved to 99%, and the accuracy of target position estimation is improved by 96% after registration. It is demonstrated that the new algorithm has excellent estimation precision of radar’s bearing system error, the convergence performance after emendating state estimation is excellent, and it has no constraint on delay error in system transmission.%在传统的雷达系统误差估计策略中，融合中心对雷达的观测数据进行处理，估计出系统误差并反馈给雷达对观测信息补偿误差。该策略会造成时延误差，不适于误差校准实时应用。为解决这一问题，构建了一种无需雷达观测信息和融合中心双向输入与反馈的雷达网络系统结构，提出了一种以雷达对目标状态估计信息为输入的系统误差协同估计算法。该算法采用扩展卡尔曼滤波对多雷达网络系统误差进行估计和配准。通过数学仿真，该方法对测角系统误差的估计精度达到了99%，目标的位置估计精度在系统误差补偿校正后达到96%，并且快速收敛。说明该方法能有效地估计出雷达的测角系统误差，并可直接校正和补偿雷达得到的状态估计信息，避免数据传输过程中产生的时延误差。
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