A Modified Algorithm for Maneuvering Target Based on Current Statistical Model Algorithm

In order to overcome the greater error of Kalman filtering algorithm in tracking non-maneuvering and weak maneuvering targets using current statistical model, a modified algorithm of acceleration variance adaptively adjusting is proposed based on further research on current statistical model. Adopting maneuver detection, the maneuver states of targets are divided into strong maneuver and weak maneuver using the statistical distance of observation residuals, acceleration variance is adjusted using modified rayleigh distribution for strong maneuver and deviation of velocity estimation and forecast for weak maneuver. The match between maneuvering model and system model is improved by using modified algorithm. The capacity of tracking strong maneuvering target is enhanced and good performance of tracking weak maneuvering target is maintained. The simulation results show that the modified algorithm has good capacity of maneuvering adaptation and good performance on tracking maneuvering target. Performance on tracking non-maneuvering and weak maneuvering targets is improved contrasted with the current statistical model conventional algorithms.