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首页> 外文会议>AIAA Guidance, Navigation and Control Conference Pt.1, Aug 1-3, 1994, Scottsdale, AZ >A Process Noise Covariance Estimator
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A Process Noise Covariance Estimator

机译：过程噪声协方差估计器

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摘要

The Kalman filter theory was developed assuming ideal conditions (i.e., all plant dynamics and noise statistics are known exactly). When this theory is applied to a particular application, these ideal assumptions do not always hold true. This paper presents a Process Noise Covariance Estimator (PNCE) algorithm that resolves the problem of applying Kalman filter theory to real world problems. This algorithm identifies an appropriate process noise covariance that yields near optimal state estimates. The PNCE algorithm is straight forward and can be easily implemented in most real world applications. All assumptions used to develop the PNCE are verified in the theory section. Three simulation examples are used to demonstrate the capabilities of this algorithm. In the first simulation, the PNCE is used to determine an appropriate covariance when only process noise is present. In the second simulation the PNCE is used to determine an appropriate covariance when only nongaussian model errors are present. In the third simulation, the PNCE is used to find an appropriate covariance when model error is a combination of deterministic and non deterministic modeling errors.
机译：卡尔曼滤波器理论是在假设理想条件的情况下开发的（即，所有设备动态和噪声统计信息都已准确了解）。当此理论应用于特定应用时，这些理想假设并不总是成立。本文提出了一种过程噪声协方差估计器（PNCE）算法，该算法解决了将Kalman滤波器理论应用于实际问题的问题。该算法识别出适当的过程噪声协方差，该协方差产生接近最佳状态的估计值。 PNCE算法简单明了，可以在大多数实际应用中轻松实现。理论部分验证了用于开发PNCE的所有假设。使用三个仿真示例来演示该算法的功能。在第一个仿真中，当仅存在过程噪声时，PNCE用于确定适当的协方差。在第二个模拟中，当仅存在非高斯模型误差时，PNCE用于确定适当的协方差。在第三次仿真中，当模型误差是确定性和非确定性建模误差的组合时，PNCE用于查找合适的协方差。

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