一种分块差分滤波器及其在仅有角度跟踪中的应用

A divided differential filter and its application in bearings-only tracking

摘要:

为解决仅有角度跟踪时，目标估计受限于较大的初始估计误差和噪声统计特性未知的问题，提出了一种带有噪声智能统计功能的改进型分块差分滤波器.通过统计线性化方法得到了一种S-H智能噪声统计估值器，并用其优化传统分块噪声滤波器的测量更新步骤，实现了对未知过程和测量噪声的智能统计处理，通过迭代更新进一步提高了滤波器对于复杂非线性函数的适应能力.与目前几类主流的自适应滤波器性能相比，结果表明:对于具有线性系统模型和非线性测量模型的典型被动跟踪估计问题，针对较大的初始状态估计误差，所给出的滤波器能更好地完成系统噪声和测量噪声部分参数统计特性未知情况下的非线性估计任务，在保证计算量适中的同时有效地提高跟踪制导精度.

Abstract:

To solve the problems of estimation accuracy restricted by large initial estimation error and unknown noise statistics in bearings-only tracking, a divided differential filter with intelligent statistical noise estimator was proposed. An S-H intelligent noise statistical estimator was proposed according to statistical linear regression theories, and was used for optimizing measurement update step of traditional divided differential filter, thus unknown state and noise measurement were intelligently and statistically calculated. The ability of the filter to adapt to complex nonlinear functions was further improved by iteration updating. Results showed that for typical passive tracking problem in linear state function and nonlinear measurement function with relative large initial estimation errors, the proposed filter provided better performance of nonlinear estimation task compared to several mainstream adaptive filters when the statistical characteristics of the system noise and measurement noise were unknown, and it effectively enhanced tracking and guidance accuracy and guaranteed moderate level of computation load at the same time.