Nonorthogonal problem in iterated unscented Kalman filter for passive tracking

2013 ◽  
Vol 8 (4) ◽  
pp. 415-419 ◽  
Author(s):  
Qin Liu ◽  
Zheng Liu ◽  
Yunfo Liu
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Passive Tracking

Joint Registration and State Estimation for Two-Station Passive Tracking

2013 ◽  
Vol 427-429 ◽  
pp. 675-679 ◽  
Author(s):  
Qiang Zhu ◽  
Jian Xun Li
Keyword(s):  
Computer Simulation ◽  
Kalman Filter ◽  
Expectation Maximization ◽  
Unscented Kalman Filter ◽  
System Model ◽  
Fusion Algorithm ◽  
Factors Affecting ◽  
Measurement Function ◽  
Passive Tracking ◽  
Locating System

Registration and nonlinearity are two crucial factors affecting the performance of the two-station passive locating system. In this paper, an online joint registration and data fusion algorithm is proposed to estimate the sensor bias and target state simultaneously using the angle-only measurements from the two ownship stations. The system model of the passive radar is firstly developed followed by the expectation-maximization (EM) approach dealing with the derivation of maximum likelihood (ML) function of the complete data. The unscented Kalman filter (UKF) is chosen to alleviate the influence caused by nonlinearity generated in the measurement function. Computer simulation shows that the proposed method is effective and reliable for this specific tracking scenario.

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