扩展卡尔曼滤波和无气味卡尔曼滤波的状态估计

2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) Pub Date : 2020-02-01 DOI:10.1109/ICONC345789.2020.9117536

Priya Shree Madhukar, L. B. Prasad

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引用次数: 25

摘要

在任何线性系统中，卡尔曼滤波器都被广泛用于跟踪和估计。扩展卡尔曼滤波比卡尔曼滤波能更好地处理非线性系统。但是扩展卡尔曼滤波器的框架并不容易绘制，它需要一些数值性质很高的项。因此，使用一种名为Unscented卡尔曼滤波器的新方法为使用sigma焦点的用户提供了一个简单的任务。采用非线性方法对系统状态进行估计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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State Estimation using Extended Kalman Filter and Unscented Kalman Filter

In any linear system the Kalman Filter is highly used to tracking and estimation. Extended Kalman Filter is deal nonlinear system better than Kalman Filter. But the framework of Extended Kalman Filter is not easy to draw they requires some highly numerical terms in nature. So, there using a new method called Unscented Kalman Filter to provide an easy task to user with use of sigma focus points. Nonlinear approach is used to estimate the state of the System.

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来源期刊

2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)

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