SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking
SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking
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DOI：
10.1631/jzus.c10a0353
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发表时间：
2011-08
期刊：
影响因子：
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通讯作者：
Quan-bo Ge;Wen-bin Li;C. Wen
中科院分区：
文献类型：
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作者：
Quan-bo Ge;Wen-bin Li;C. Wen
平方根容积卡尔曼滤波（SCKF）是一种比无迹卡尔曼滤波更有效的非线性状态估计方法。本文研究了一阶噪声相关和状态突变系统的基于SCKF的非线性滤波器设计问题。首先，我们给出了处理过程噪声和测量噪声之间的一步相关的SCKF，简称SCKF-CN。其次，引入强跟踪滤波器的思想，构造具有衰减因子的预测误差协方差的自适应平方根因子，使得SCKF-CN对目标机动或状态突变的系统具有优异的跟踪性能。因此，SCKF的跟踪性能得到了很大的改善。提出了一种通用的非线性估计器，它不仅能处理高维相关噪声下的常规非线性滤波问题，而且对目标状态突变具有很强的跟踪性能。三个仿真例子与纯方位跟踪系统的说明，以验证所提出的算法的效率。
Square-root cubature Kalman filter (SCKF) is more effective for nonlinear state estimation than an unscented Kalman filter. In this paper, we study the design of nonlinear filters based on SCKF for the system with one step noise correlation and abrupt state change. First, we give the SCKF that deals with the one step correlation between process and measurement noises, SCKF-CN in short. Second, we introduce the idea of a strong tracking filter to construct the adaptive square-root factor of the prediction error covariance with a fading factor, which makes SCKF-CN obtain outstanding tracking performance to the system with target maneuver or abrupt state change. Accordingly, the tracking performance of SCKF is greatly improved. A universal nonlinear estimator is proposed, which can not only deal with the conventional nonlinear filter problem with high dimensionality and correlated noises, but also achieve an excellent strong tracking performance towards the abrupt change of target state. Three simulation examples with a bearings-only tracking system are illustrated to verify the efficiency of the proposed algorithms.