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采用基于蒙特卡罗近似的贝叶斯估计算法处理地形辅助导航系统中的估计问题 ,避免了对复杂地形的线性化过程。数字仿真结果表明该方法比传统的基于扩展卡尔曼滤波的SITAN算法有更高的估计精度 ,并能有效克服初始位置误差较大时 ,基于地形线性化方法易出现的滤波发散问题。
The Bayesian estimation algorithm based on Monte Carlo approximation is used to deal with the estimation problem in terrain-assisted navigation system, which avoids the linearization of complex terrain. The numerical simulation results show that this method has higher estimation accuracy than the traditional SIFT algorithm based on extended Kalman filter and can effectively overcome the filtering divergence problem based on the terrain linearization method when the initial position error is large.