ILS算法具有噪音参量估计准确度高、收敛速度快和计算复杂度低等优点。
The ILS algorithm had the strength of accurate estimation of FPN, fast convergence rate and low computational complexity.
移动与静止目标获取与识别（MSTAR）公共数据库实测数据的仿真结果表明，该方法估计误差较小，可获得较高的估计准确度。
The simulation results using Moving and Stationary Target Acquisition and Recognition (MSTAR) data indicate that the error of the proposed method is small, and thus it has high accuracy.
详细分析与估计了系统的测量误差并探索了提高准确度的方法。
The error have been analyzed and estimated in detail and the method to raise accuracy of measurement have been discussed.
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