Research On Maneuvering Target Tracking Based On Interacting Multiple Model For Multiple Passive Sensors

Maneuvering target tracking based on multiple passive sensors is one of important aspects in the field of target tracking, which has been paid attention by domestic and foreign scholars and experts. With the development of modern science and technology and the unceasing enhancement of the target maneuver performance, modern war environment is increasingly complex, which put forward higher requirement to target tracking. Therefore, the research of the maneuvering target tracking based on multiple passive sensors is of great significance to enhancing the capacity of China's defense system. Aiming at maneuvering targets maneuvering characteristic, this dissertation focuses on Interacting Multiple Model (IMM) algorithm.Firstly, aiming at the nonlinear observation problem of multiple passive sensors, this dissertation involves researches about the recently proposed Gaussian Filter algorithm based on deterministic sampling, and elaborates in detail on utilizing Lagrange multiplier method so as to attain sampling points which is close to the state a priori probability density function. Combined with IMM algorithm modular design, the Interacting Multiple Model Gaussian filter algorithm (IMMGF) based on multiple passive sensors is proposed, which can effectively improve the accuracy of maneuvering target tracking.Secondly, in view of the imprecise estimated problem of the target's velocity and acceleration resulting from the scalar weight to update model in the IMM algorithm, this dissertation involves researches about diagonal interacting multiple model (DIMM) algorithm, which utilizes the optimal information fusion theory so as that each dimension element of the state estimation vector (such as:position, velocity, acceleration, etc.) achieves optimal integration and state fusion error covariance matrix is minimized. In combination with the Unscented Kalman filter (UKF) algorithm, the DIMMUKF algorithm is proposed, which effectively improved the accuracy of maneuvering target tracking, especially for the target speed, acceleration, etc.Thirdly, this dissertation involves researches about a new Interacting Multiple Model with Switch Time Conditions (STC-IMM). When the sampling rate of the underlying continuous process is high compared to the target dynamics, the maneuver tracking performance of STC-IMM algorithm is significantly superior to the IMM algorithm. The IMM algorithm in combination with Extended Kalman Filter (EKF) applied to the passive multi-sensor maneuvering target tracking, a novel STC-IMM-EKF algorithm is proposed, which can achieve maneuvering target tracking in multi-sensor bearing-only tracking.Finally, to solve the problem that the model for maneuvering target tracking is difficult to match, an adaptive tracking algorithm based on curve model for maneuvering target tracking is proposed. It makes some improvements to the present curve model adaptive tracking algorithm, and then effectively avoids the filter divergence resulting from the imprecise estimates to the model. Simulation results show that the proposed algorithm has higher precision and broader scope of application than the existing algorithm.