Abstract:

The reasons why Kalman filter’s estimation precision is low and the filter is divergent when the target moving state changes or the moving model is not accurately established are analyzed. In contrast with the adaptive fading Kalman filter, a new amending algorithm is put forward which improves Kalman filter estimation accuracy and performance by adjusting the predicted value directly. The amending algorithm adjusts the predicted value in time by setting judgment and amendment rules, which can quickly reduce estimation error in the initiating filter stage, increase the precision in the static filter stage and shorten the convergence time. The new algorithm is able to reduce or eliminate the situation where the tracking precision declines and the filter is divergent when the moving state changes. Also, it can reduce or eliminate the model error when the moving model is not accurately established. The results of simulation indicate the effectiveness of the new algorithm and its strong practical guiding significance.