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首页> 外文期刊>Journal of electronic imaging >Recursive update filtering-based variational Bayesian approach for extended target or group target tracking with nonlinear measurements
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Recursive update filtering-based variational Bayesian approach for extended target or group target tracking with nonlinear measurements

机译：基于递归更新过滤的变分贝叶斯方法，用于扩展目标或基于非线性测量的目标跟踪

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摘要

For extended target and group target tracking, most existing random matrix approaches assume that the measurements are linear in the kinematic state and in the noise with its covariance being a random matrix to represent the extension of an extended target or target group. However, in practice, the measurements (e.g., range and bearing) are nonlinear in the kinematic state and extension noise. Only the matched linearization (ML) approach has been proposed to linearize the nonlinear measurement function based on minimum mean square error criterion. However, if the state prediction error is not small, it will incur large linearization error and then the variational Bayesian measurement update approach produces inaccurate estimation results. To this point, using the ML technology, we present a recursive update filtering-based variational Bayesian update (RUF-VBU) approach for better estimation performance. In addition, the square-root implementation of RUF-VBU is proposed for the recursive interruption problem caused by the nonpositive definite covariance matrix. The simulation results demonstrate the effectiveness of the proposed approach. (C) 2019 SPIE and IS&T
机译：对于扩展目标和组目标跟踪，大多数现有的随机矩阵方法假设测量在运动状态中是线性的，并且在噪声中，其协方差是随机矩阵来表示扩展目标或目标组的扩展。然而，在实践中，测量（例如，范围和轴承）在运动状态和延伸噪声中是非线性的。已经提出仅基于最小均方误差标准来线性化非线性测量函数的匹配线性化（ML）方法。但是，如果状态预测误差不小，则会产生大的线性化误差，然后变分贝叶斯测量更新方法产生不准确的估计结果。为此，使用ML技术，我们介绍了一种基于递归更新过滤的变分贝叶斯更新（RUF-VBU）方法，以获得更好的估计性能。此外，提出了由非阳性明确协方差矩阵引起的递归中断问题的ruf-vbu的平方根实现。仿真结果表明了所提出的方法的有效性。 （c）2019 SPIE和IS＆T

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