基于擴展Kalman濾波的單領航者自主水下航行器協(xié)同導航判別式訓練方法研究
doi: 10.11999/JEIT150036 cstr: 32379.14.JEIT150036
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2.
(中國科學院聲學研究所 北京 100190) ②(中國科學院大學 北京 100190)
基金項目:
國家自然科學基金(61372180)
Discriminative Training of Kalman Filters Based Cooperative Navigation for Multiple Autonomous Underwater Vehicles with a Single Leader
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2.
(中國科學院聲學研究所 北京 100190)
Funds:
The National Natural Science Foundation of China (61372180)
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摘要: 單領航者自主水下航行器(AUV)協(xié)同導航算法中，系統(tǒng)模型是非線性的，擴展Kalman濾波(EKF)是針對非線性系統(tǒng)的很有影響力的濾波算法，但是，EKF算法的性能嚴格依賴于一系列模型參數(shù)，而這些參數(shù)往往需要花費很大的代價來捕獲，并且常需要人工調整。該文應用一種能自動學習Kalman濾波噪聲協(xié)方差參數(shù)的方法，通過仿真分析，證明了該學習算法可以完全自主并且高效、準確地輸出Kalman濾波噪聲參數(shù)，進一步提高了單領航者AUV協(xié)同導航系統(tǒng)的導航精度。
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關鍵詞:
- 自主水下航行器 /
- 協(xié)同導航 /
- 擴展Kalman濾波 /
- 自動學習噪聲參數(shù)
Abstract: In the cooperative navigation algorithm for multiple Autonomous Underwater Vehicles (AUVs) with a single leader, the model of the system is nonlinear. The Extended Kalman Filter (EKF), which is directed against the nonlinear system, is one of the most influential techniques. However, the performance of EKF critically depends on a large number of modeling parameters which can be very difficult to obtain, and are often set by manual tweaking and at a great cost. In this paper, a method for automatically learning the noise covariance of a Kalman filter is applied, and the simulation result shows that this algorithm fully automatically and quickly outputs the noise covariance, which improves the navigation accuracy of the cooperative navigation system. -
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