| 变分优化的高斯混合滤波及其在导航中的应用 |
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| 引用本文： | 戴卿,隋立芬,田源,田翌君,曾添.变分优化的高斯混合滤波及其在导航中的应用[J].武汉大学学报(信息科学版),2019,44(5):699-705. |
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| 作者姓名： | 戴卿 隋立芬 田源 田翌君 曾添 |
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| 作者单位： | 1.信息工程大学地理空间信息学院, 河南 郑州, 450001 |
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| 基金项目： | 国家自然科学基金41674016国家自然科学基金41274016 |
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| 摘 要： | 针对时间差分载波相位/捷联惯导紧组合系统在非高斯噪声环境工作时，采用高斯混合滤波遇到的混合模型参数估计问题，提出了一种变分贝叶斯学习优化的高斯混合自适应滤波算法。该算法借鉴变分学习理论，准确高效地实现了高斯混合模型参数的自适应估计，进一步精化了滤波算法中的随机模型，能够显著提高估计精度，降低计算负担，改善滤波性能。实验结果表明，相比传统滤波算法，该算法的估计精度得到了进一步改善，运算耗时仅与拓展卡尔曼滤波相当。
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| 关 键 词： | 变分贝叶斯学习 高斯混合滤波 时间差分 自适应滤波 |
| 收稿时间： | 2018-05-05 |
Gaussian Mixture Filter Based on Variational Bayesian Learning Optimization and Its Application to Integrated Navigation |
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| Institution: | 1.Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China |
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| Abstract: | We present a novel Gaussian mixture filter (GMF) improved by variational Bayesian learning. The method is mainly used in time-differenced carrier phase/strap-down Inertial Navigation System integrated navigation to deal with parameter estimation of Gaussian mixture model in non-Gaussian noise environment. By means of variational Bayesian learning theory, this algorithm accurately and efficiently estimates parameters of GMF, and further refines the stochastic model. And it improves accuracy of GMF, reduces the computational complexity, and improves the effectiveness. The experimental results illustrate that the proposed filter substantially outperforms the existing algorithm in terms of estimation accuracy, and is computationally much more efficient. It provides theoretical support for integrated navigation data fusion strategy based on GMF. |
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