Application of UKF Alogrithm in Passive Localization and Tracking

Depending on the information of DOA and TOA, passive location to a moving emitter can be realized by a fixed observer. In the location system, the state model is generally linear, but the measurement model is frequently nonlinear. With the help of extended Kalman filter (EKF), the state of the target can be estimated, and the track of it can be also traced. Unscented Kalman filter(UKF) is another method to solve the problem of nonlinear system. Deduction and simulation shows that, using EKF and UKF based on DOA and TOA information to locate and track the moving emitter, can be convergent with a high accuracy. Relatively, UKF is better than EKF.

[1]  Mark L. Fowler Analysis of single-platform passive emitter location with terrain data , 2001 .

[2]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[3]  Doo-Seop Eom,et al.  A TDoA-based localization using precise time-synchronization , 2012, 2012 14th International Conference on Advanced Communication Technology (ICACT).

[4]  Qun Wan,et al.  Time-difference-of-arrival estimation for noncircular signals using information theory , 2013 .

[5]  Karl Johan Åström,et al.  Estimation and Optimal Configurations for Localization Using Cooperative UAVs , 2008, IEEE Transactions on Control Systems Technology.

[6]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.