|Table of Contents|

Admissible baseline measurement error of variance-constrained filter in distributed track fusion


Research Field:
Publishing date:


Admissible baseline measurement error of variance-constrained filter in distributed track fusion
Wu YungangTang Zhenmin
School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
distributed fusion track baseline measurement devices steady-state Kalman filter linear matrix inequality
To determine the baseline measurement device in distributed track fusion,admissible baseline measurement errors with given constrained variance is researched.A convergence condition of the stability of the estimation error covariance matrix is derived based on the steady-state Kalman filter theory.A steady-state Kalman filter design method satisfying the desired requirements is proposed using a linear matrix inequality(LMI)method.Designers can choose a cheaper baseline measurement device with lower precision to satisfy the system design accuracy.A numerical example shows the efficiency of the proposed approach.


[1] Mori S,Chang Kuochu,Chong C Y.Comparison of track fusion rules and track association metrics[C]//15th International Conference on Information Fusion.Singapore:IEEE,2012:1996-2003.
[2]Zhang Yu,Ran Jinhe.Dynamic weighted track fusion algorithm based on track comparability degree[C]//IEEE International Conference on Information Theory and Information Security.Beijing:IEEE,2010:710-713.
[3]Vaccarella A,De Momi E,Enquobahrie A,et al.Unscented Kalman filter based sensor fusion for robust optical and electromagnetic tracking in surgical navigation[J].IEEE Transactions on Instrumentation and Measurement,2013,62(7):2067-2081.
Qiao Xiangdong,Li Tao.Survey of multi-sensor track fusion[J].Systems Engineering and Electronics,2009,31(2):245-250.
[5]Bar-Shalom Y.On hierarchical tracking for the real world[J].IEEE Transactions on Aerospace and Electronic Systems,2006,42(3):846-850.
[6]Saha R K,Chang K C.An efficient algorithm for multisensory track fusion[J].IEEE Transactions on Aerospace and Electronic Systems,1998,34(1):200-210.
[7]Juiler S,Uhlamann J.General decentralized data fusion with co-variance intersection(CI)[C]//Handbook of Multisensor Data Fusion.Boca Raton,FL,USA:CRC Press,2001.
[8]Hurely M B.An information theoretic justification for covariance intersection and its generalization[C]//Proceedings of the 5th International Conference on Information Fusion.Annapolis,MD,USA:IEEE,2002:505-511.
[9]Chong C Y,Mori S.Convex combination and covariance inter-section algorithms in distributed fusion[C]//Proceedings of the 4th International Conference on Information Fusion.Montreal,QC,Canada:IEEE,2001:111-118.
[11]Chang K C,Tian Z,Saha R K.Performance evaluation of track fusion with information matrix filter[J].IEEE Transactions on Aerospace and Electronic Systems,2002,38(2):455-466.
[12]Aeberhard M,Schlichthaerle S,Kaempchen N A.Track-to-track fusion with asynchronous sensors using information matrix fusion for surround environment perception[J].IEEE Transactions on Intelligent Transportation Systems,2012,13(4):1717-1726.
[13]Vo B,Cantoni A.Analytic implementations of the cardinalized probability hypothesis density filter[J].IEEE Transactions on Signal Processing,2007,55(7):3553-3567.
Sheng Andong,Wang Yuangang,Liu Jian,et al.Admissible model noise of variance-constrained filter in a trajectory identification system[J].Conrol and Decision,2001,16(5):553-556.
Chen Sujuan,Qi Guoqing,Sheng Andong.Admissible sampling frequency in measurements with variance-constrained and missing data[J].Control Theory & Applications,2012,29(5):629-634.


Last Update: 2016-06-30