The invention provides a KF (
Kalman Filter) tracking method based on
fading memory
exponential weighting. The method comprises the following steps: a state error
covariance matrix P and a
systematic process noise matrix are acquired; an estimated predictive
state parameter value shown in the description of a moving object at the moment k is calculated, and innovation
covariance C0,k at the momentk is calculated; innovation gamma k at the moment k is calculated, an estimated innovation
covariance value shown in the description at the moment k is calculated,
weighting coefficient beta k at themoment k is calculated, and the
fading factor
lambda k at the moment k is further calculated; a predictive state error
covariance matrix Pk|k-1 and Kalman
gain Kk at the moment k are calculated, and an estimated state value shown in the description and a state error
covariance matrix Pk are further calculated, wherein a calculation method for the estimated innovation covariance value at the momentk is shown in the description, and the
weighting coefficient [beta i] decays following the law of negative exponent. The problem of poorer precision of the traditional windowing average method for calculating innovation residual vector
estimation is solved, and innovation residual
estimation precision is improved effectively, so that the method has higher precision and robustness.