scholarly journals Multiple extended stealth target tracking from image observation based on non‐linear sub‐random matrices approach

2021 ◽  
Vol 15 (3) ◽  
pp. 274-293
Author(s):  
Mohamed Barbary ◽  
Mohamed H. Abd ElAzeem
Keyword(s):  
Target Tracking ◽  
Random Matrices ◽  
Non Linear

Non-linear automatic target tracking in clutter using dynamic Gaussian mixture

2012 ◽  
Vol 6 (9) ◽  
pp. 937-944 ◽  
Author(s):  
D. Mušicki ◽  
T.L. Song ◽  
W.C. Kim ◽  
D. Nešić
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Non Linear

Particle filter initialization in non-linear non-Gaussian radar target tracking

2007 ◽  
Vol 18 (3) ◽  
pp. 491-496 ◽  
Author(s):  
Wang Lian ◽  
Jin Yonggao ◽  
Dai Dingzhang ◽  
Dong Huachun ◽  
Quan Taifan
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Radar Target ◽  
Non Linear ◽  
Non Gaussian

Locality-Sensitive Non-Linear Kalman Filter for Target Tracking

2021 ◽  
Vol 13 (1) ◽  
pp. 36-57
Author(s):  
Ben-Bright Benuwa ◽  
Benjamin Ghansah
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Traffic Control ◽  
Unscented Kalman Filter ◽  
Research Area ◽  
Linear Filtering ◽  
Tracking Accuracy ◽  
Propagation Of Uncertainty ◽  
Non Linear ◽  
Marine Engineering

Target tracking (TT) with non-linear kalman filtering (NLKF) has recently become a very popular research area, particularly in the field of marine engineering and air traffic control. Contemporary NLKF algorithms have been very effective, in particular, with extensions and merging with a reduced root mean square error (RMSE) value. However, there are a number of issues that confront NLKF approaches, notably weakness in robustness, convergence speed, and tracking accuracy due to large initial error and weak observability. Furthermore, NLKF algorithms significantly results in error for high non-linear systems (NLS) because of the propagation of uncertainty. Again, there is a problem of estimating future states as a result of white noise. To handle these issues, the authors propose a novel non-linear filtering algorithm, called locality-sensitive NLKF (LSNLKF) that incorporates locality-sensitive adaptors into the structure of an integrated NLKF. They are the extended kalman filter (EKF) and the unscented kalman filter (UKF) for TT.

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