Deterministic and stochastic Bayesian methods in terrain navigation
Deterministic and stochastic Bayesian methods in terrain navigation
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DOI：
10.1109/cdc.1998.760675
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发表时间：
1998-12
期刊：
影响因子：
--
通讯作者：
N. Bergman
中科院分区：
文献类型：
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作者：
N. Bergman
地形导航是一个应用程序，在概念上不同的传感器之间的推理递归在线执行。在这项工作中，贝叶斯框架的统计推断适用于这个递归估计问题。在模拟中评估了近似贝叶斯估计的三种算法，一种确定性算法和两种随机算法。确定性方法通过数值积分来解决贝叶斯推断问题，而随机方法模拟多个候选解并通过对这些候选解进行平均来评估积分。仿真结果表明，这三种算法都是有效的，并且都能达到Cramer-Rao界。然而，随机方法对异常值敏感，而确定性方法则存在难以在高维中实现的局限性。
Terrain navigation is an application where inference between conceptually different sensors is performed recursively online. In this work the Bayesian framework of statistical inference is applied to this recursive estimation problem. Three algorithms for approximative Bayesian estimation are evaluated in simulations, one deterministic algorithm and two stochastic. The deterministic method solve the Bayesian inference problem by numerical integration while the stochastic methods simulate several candidate solutions and evaluates the integral by averaging between these candidates. Simulations show that all three algorithms are efficient and approximately reach the Cramer-Rao bound. However, the stochastic methods are sensitive to outliers and the deterministic method has the limitation of being hard to implement in higher dimensions.