自校正多传感器观测融合Kalman估值器及其收敛性分析

Self-tuning multisensor measurement fusion Kalman estimator and its convergence analysis

中文关键词  多传感器信息融合  加权观测融合  自校正Kalman估值器  噪声方差估计  收敛性分析  现代时间序列分析方法

英文关键词  multisensor information fusion  weighted measurement fusion  self-tuning Kalman estimator  noise variance estimation  convergence analysis  modern time series analysis method

中文摘要

      对于带未知噪声方差的多传感器系统, 应用加权最小二乘(WLS)法得到了一个加权融合观测方程, 且它与状态方程构成一个等价的观测融合系统. 应用现代时间序列分析方法, 基于观测融合系统的滑动平均(MA)新息模型参数的在线辨识, 可在线估计未知噪声方差, 进而提出了一种加权观测融合自校正Kalman估值器, 可统一处理自校正融合滤波、预报和平滑问题, 并用动态误差系统分析方法证明了它的收敛性, 即若MA新息模型参数估计是一致的, 则它按实现或按概率1收敛到全局最优加权观测融合Kalman估值器, 因而具有渐近全局最优性. 一个带3传感器跟踪系统的仿真例子说明了其有效性.

英文摘要

      For the multisensor system with unknown noise variances, by the weighted least squares (WLS) method, a weighted fused measurement equation is obtained. Together with the state equation, it thus constitutes an equivalent measurement fusion system. Using the modern time series analysis method, based on on-line identification of the moving average (MA) innovation model parameters for the measurement fusion system, the online estimators of noise variances can be obtained, and a self-tuning weighted measurement fusion Kalman estimator is presented, which can handle the selftuning fused filtering, prediction, and smoothing problems in a unified framework. Its convergence is also proved by using the dynamic error system analysis method, i.e. if the parameter estimation of the MA innovation model is consistent, then it will converge to a globally optimally weighted measurement Kalman estimator, in a realization or with probability one. Consequently it has asymptotic global optimality. A simulation example for a tracking system with 3 sensors shows its effectiveness.