Multi-method fusion based Kalman filtering quantization method

Abstract

The invention relates to a multi-method fusion based Kalman filtering quantization method. The multi-method fusion based Kalman filtering quantization method comprises three parts of contents. The first part comprises performing system modelling according to real target motions; a second part comprises consulting pertinent literatures and giving optimal estimation results to QSK-STF and VB-AQKF; a third part comprises achieving optimal linear weighing fusion through the QSK-STF, wherein the optimal linear weighing fusion comprises calculating an optimal weighing matrix, estimating a final target state weighing fusion state and fusion estimating error covariance and a cross covariance matrix. The multi-method fusion based Kalman filtering quantization method has a strong trace function, can perform dynamic estimation on unknown covariance, achieves online real-time estimation and improves the target trace accuracy and accordingly the multi-method fusion based Kalman filtering quantization method can accurately estimate motion states of a target at any moment according to the existing data measured by a radar and achieves a target trace function.

Description

technical field [0001] The invention belongs to the field of target tracking of linear systems, in particular to a quantized Kalman filtering method based on multi-method fusion. Background technique [0002] Linear filtering theory is widely used in application fields such as target tracking, information processing and fault diagnosis, and its development is quite mature compared with nonlinear filtering. In particular, in the context of the emergence of a large number of distributed sensor network systems, quantitative filtering and fusion have become hot research topics in the fields of signal processing and control. [0003] The Kalman filter (KF) was originally proposed by R.E Kalman when dealing with the state estimation of linear dynamic systems. It is based on the accurate model, the known statistical characteristics of random interference signals and the absence of sudden changes in the state. However, in the actual system, there are often these uncertain factors, ...

AI Technical Summary

Problems solved by technology

When the state is abrupt and the variance of the estimated quantization error is unknown, STF automatically adjusts the one-step prediction error covariance by introducing a fading factor to effectively track the state and achieve a strong tracking function and improve the estimation accuracy, but it cannot estimate the measurement unknown variance of the noise

And VB can estimate the unknown variance of the measurement noise online in real time, which improves the estimation accuracy of the system, but the robustness performance is poor

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