Based on EX algorithm for error estimation of fixed multiple sensors, and considering the Euler angle bias of the airborne sensor, we proposed an improved error estimation algorithm, named as EEX, for estimating the error of airborne multi-sensor system exactly. Extended Kalman Filer (EKF) was used to linearize the non-linear measurement equation. The same random point targets were used, the decoulpling between the target state estimation and the sensor bias estimation was achieved without approximating the cross-covariance between the state estimate and the bias estimate. Simulation results show that: compared with MLRM method, EEX algorithm improves the estimation accuracy by nearly 30%. In addition, the additive noises of pseudo-measurements of the sensor bias obtained through state estimation are all white Gaussian noise with zero-mean and known variance, thus the estimation result is very close to the Cramer-Rao Lower Bound (CRLB), which means that the proposed estimation is statistically efficient.