scholarly journals Improved Student’s t-Based Unscented Filter and its Application to Trajectory Estimation for Maneuvering Target

2019 ◽  
Vol 9 (11) ◽  
pp. 2186 ◽  
Author(s):  
Xiaohang Wu ◽  
Kemao Ma
Keyword(s):  
State Vector ◽  
Unscented Kalman Filter ◽  
Measurement Model ◽  
Maneuvering Target Tracking ◽  
Conditional Probability Distribution ◽  
Maneuvering Target ◽  
Delayed Measurement ◽  
One Step ◽  
Student’S T ◽  
Unscented Filter

To estimate the systems with one-step randomly delayed measurement and time-correlated heavy-tailed measurement noises, on the basis of robust Student’s t based unscented filter (RSTUF), an improved Student’s t based unscented filter (ISTUF) is proposed. Referring to the measurement differencing method, a reformed measurement model was built. Then, by augmenting the system state vector, the conditional probability distribution of the measurement noise with respect to delayed measurement was taken into consideration. Based on the reformed measurement model and the augment state vector, a novel estimator was designed to solve the one-step randomly delayed problem. Maneuvering target tracking systems were used for simulation. Compared with unscented Kalman filter (UKF) or RSTUF, ISTUF had higher accuracy.

A Maneuvering Target Tracking Algorithm Based on Gaussian Filter for Multiple Passive Sensors

2011 ◽  
Vol 467-469 ◽  
pp. 447-452
Author(s):  
Jin Long Yang ◽  
Hong Bing Ji ◽  
Jin Mang Liu
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Nonlinear Problem ◽  
Unscented Kalman Filter ◽  
Tracking Algorithm ◽  
Gaussian Filter ◽  
Maneuvering Target Tracking ◽  
Selection Scheme ◽  
Maneuvering Target ◽  
Passive Sensors

When tracking a maneuvering target by multiple passive sensors, two problems need to be considered, one is the nonlinear problem, another is the maneuvering problem. Taking these into account, a Gaussian filter (GF) for nonlinear Bayesian estimation is introduced based on a deterministic sample selection scheme, which can solve the nonlinear problem better than the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Then, a new maneuvering target tracking algorithm is proposed based on the GF and Interacting Multiple Mode (IMM), called IMM-GF method in this paper. Simulation results show that the proposed method has better performance than the IMM-EKF and IMM-UKF in tracking a maneuvering target for multiple passive sensors.

scholarly journals Maneuvering Target Tracking Using Simultaneous Optimization and Feedback Learning Algorithm Based on Elman Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1596 ◽  
Author(s):  
Huajun Liu ◽  
Liwei Xia ◽  
Cailing Wang
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Learning Algorithm ◽  
Simultaneous Optimization ◽  
Optimization Strategy ◽  
Maneuvering Target Tracking ◽  
Elman Neural Network ◽  
Maneuvering Target ◽  
Feedback Learning

Tracking maneuvering targets is a challenging problem for sensors because of the unpredictability of the target’s motion. Unlike classical statistical modeling of target maneuvers, a simultaneous optimization and feedback learning algorithm for maneuvering target tracking based on the Elman neural network (ENN) is proposed in this paper. In the feedback strategy, a scale factor is learnt to adaptively tune the dynamic model’s error covariance matrix, and in the optimization strategy, a corrected component of the state vector is learnt to refine the final state estimation. These two strategies are integrated in an ENN-based unscented Kalman filter (UKF) model called ELM-UKF. This filter can be trained online by the filter residual, innovation and gain matrix of the UKF to simultaneously achieve maneuver feedback and an optimized estimation. Monte Carlo experiments on synthesized radar data showed that our algorithm had better performance on filtering precision compared with most maneuvering target tracking algorithms.

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