Fast Multiple Asymptotic Factorized Strongly Tracked Volume Kalman Filter Algorithm Based on the Innovation Discriminant

Currently in the main algorithms of radar tracking for strong maneuvering targets, the main problems are low real-time performance and large tracking errors. Aiming at the tracking problem of strongly maneuvering targets, this paper proposes fast numerous fading factors based on the multiple fading factors strong tracking square-root cubature Kalman filter algorithm (MSTSRCKF) combined with innovation discrimination (FMSTSRCKF).On the one hand, this algorithm overcomes the problem that SRCKF will cause the filtering effect to decrease when the tracking target maneuvers. On the other hand, it overcomes the shortcomings of MSTSRCKF, which is time-consuming and has low accuracy. Simulation experiments show that compared with SRCKF and MSTSRCKF, the method proposed in this paper considers the requirements of high accuracy and low time consumption for tracking strongly maneuvering targets.