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带乘性噪声的欠观测系统无迹增量Kalman融合估计

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研究了带乘性噪声的非线性欠观测系统的多传感融合估计问题.采用虚拟状态向量与虚拟噪声,并为虚拟状态设计一步预报器与状态更新方程.针对非线性欠观测系统提出了无迹增量Kalman滤波算法(UIKF).提出了对角矩阵加权的融合增量卡尔曼滤波器.通过对比分析,得到增量估计值精度要高于标准估值精度,加权融合曲线的估值精度要高于单一子传感器估值精度,验证了在滤波过程中使用增量滤波方法对状态估计的优化.
Unscented incremental Kalman fusion estimation for under-observed systems with multiplicative noise
The fusion estimation problem was studied for the multi-sensor nonlinear under-observed systems with multiplicative noises.The virtual state vector and virtual noise were adopted,and one-step predictor and state update equation were designed for the virtual state.An unscented incremental Kalman filter(UIKF)was proposed for the nonlinear under-observed systems.The fusion incremental Kalman filters weighted by diagonal matrices were proposed.Through comparative analysis,the incremental estimate is higher than the stardard estimate precision,and the weighted fusion curve is higher than each sensor estimate precision.The comparison analysis was made to verify the optimization of the state estimation using incremental filtering method in the filtering process.

information fusionmultiplicative noiseunder-observed systemunscented Kalman filteringincremental filtering

崔永鹏、孙小君、张扬

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黑龙江大学 电子工程学院,哈尔滨 150080

黑龙江信息融合估计与检测重点实验室,哈尔滨 150080

黑龙江大学 机电工程学院,哈尔滨 150080

信息融合 乘性噪声 欠观测系统 无迹Kalman滤波 增量滤波

国家自然科学基金黑龙江省高等学校基本科研业务费专项黑龙江省科技厅揭榜挂帅项目黑龙江省高等学校基本科研业务费专项黑龙江大学校级教学改革项目

611042092020-KYYWF-00982023ZX07B012021-KYYWF-00262021C21

2024

10.13524/j.2097-2873.2024.02.021

黑龙江大学工程学报
黑龙江大学

黑龙江大学工程学报

影响因子：0.358
ISSN：2095-008X
年,卷(期)：2024.15(2)
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